def explain(args, model, processor, tokenizer, output_mode, label_list, device): """ Added into run_model.py to support explanations :param args: configs, or args :param model: The model to be explained :param processor: For explanations on Gab/WS etc. Dataset, take an instance of Processor as input. See Processor for details about the processor :param tokenizer: The default BERT tokenizer :param output_mode: "classification" for Gab :param label_list: "[0,1]" for Gab :param device: An instance of torch.device :return: """ assert args.eval_batch_size == 1 processor.set_tokenizer(tokenizer) if args.algo == 'soc': try: train_lm_dataloder = processor.get_dataloader( 'train', configs.train_batch_size) dev_lm_dataloader = processor.get_dataloader( 'dev', configs.train_batch_size) except FileNotFoundError: train_lm_dataloder = None dev_lm_dataloader = None explainer = SamplingAndOcclusionExplain( model, configs, tokenizer, device=device, vocab=tokenizer.vocab, train_dataloader=train_lm_dataloder, dev_dataloader=dev_lm_dataloader, lm_dir=args.lm_dir, output_path=os.path.join(configs.output_dir, configs.output_filename), ) else: raise ValueError label_filter = None if args.only_positive and args.only_negative: label_filter = None elif args.only_positive: label_filter = 1 elif args.only_negative: label_filter = 0 if not args.test: eval_examples = processor.get_dev_examples(args.data_dir, label=label_filter) else: eval_examples = processor.get_test_examples(args.data_dir, label=label_filter) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer, output_mode, configs) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_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) if output_mode == "classification": all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) elif output_mode == "regression": all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) if args.hiex_idxs: with open(args.hiex_idxs) as f: hiex_idxs = json.load(f)['idxs'] print('Loaded line numbers for explanation') else: hiex_idxs = [] model.train(False) for i, (input_ids, input_mask, segment_ids, label_ids) in tqdm(enumerate(eval_dataloader), desc="Evaluating"): if i == args.stop: break if hiex_idxs and i not in hiex_idxs: continue input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) if not args.hiex: explainer.word_level_explanation_bert(input_ids, input_mask, segment_ids, label_ids) else: explainer.hierarchical_explanation_bert(input_ids, input_mask, segment_ids, label_ids) if hasattr(explainer, 'dump'): explainer.dump()
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help= "The input data dir. Should contain the .tsv files (or other data files) for the task." ) 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-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.") parser.add_argument( "--output_dir", default=None, type=str, required=True, help= "The output directory where the model predictions and checkpoints will be written." ) parser.add_argument("--negative_weight", default=1., type=float) parser.add_argument("--neutral_words_file", default='data/identity.csv') # if true, use test data instead of val data parser.add_argument("--test", action='store_true') # Explanation specific arguments below # whether run explanation algorithms parser.add_argument("--explain", action='store_true', help='if true, explain test set predictions') parser.add_argument("--debug", action='store_true') # which algorithm to run parser.add_argument("--algo", choices=['soc']) # the output filename without postfix parser.add_argument("--output_filename", default='temp.tmp') # see utils/config.py parser.add_argument("--use_padding_variant", action='store_true') parser.add_argument("--mask_outside_nb", action='store_true') parser.add_argument("--nb_range", type=int) parser.add_argument("--sample_n", type=int) # whether use explanation regularization parser.add_argument("--reg_explanations", action='store_true') parser.add_argument("--reg_strength", type=float) parser.add_argument("--reg_mse", action='store_true') # whether discard other neutral words during regularization. default: False parser.add_argument("--discard_other_nw", action='store_false', dest='keep_other_nw') # whether remove neutral words when loading datasets parser.add_argument("--remove_nw", action='store_true') # if true, generate hierarchical explanations instead of word level outputs. # Only useful when the --explain flag is also added. parser.add_argument("--hiex", action='store_true') parser.add_argument("--hiex_tree_height", default=5, type=int) # whether add the sentence itself to the sample set in SOC parser.add_argument("--hiex_add_itself", action='store_true') # the directory where the lm is stored parser.add_argument("--lm_dir", default='runs/lm') # if configured, only generate explanations for instances with given line numbers parser.add_argument("--hiex_idxs", default=None) # if true, use absolute values of explanations for hierarchical clustering parser.add_argument("--hiex_abs", action='store_true') # if either of the two is true, only generate explanations for positive / negative instances parser.add_argument("--only_positive", action='store_true') parser.add_argument("--only_negative", action='store_true') # stop after generating x explanation parser.add_argument("--stop", default=100000000, type=int) # early stopping with decreasing learning rate. 0: direct exit when validation F1 decreases parser.add_argument("--early_stop", default=5, type=int) # other external arguments originally here in pytorch_transformers parser.add_argument( "--cache_dir", default="", type=str, help= "Where do you want to store the pre-trained models downloaded from s3") parser.add_argument( "--max_seq_length", default=128, type=int, help= "The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument( "--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=32, type=int, help="Total batch size for eval.") parser.add_argument("--validate_steps", default=200, type=int, help="validate once for how many steps") 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("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") 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( '--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument( '--loss_scale', type=float, default=0, help= "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() combine_args(configs, args) args = configs if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() processors = { 'gab': GabProcessor, 'ws': WSProcessor, 'nyt': NytProcessor, 'MT': MTProcessor, #'multi-label': multilabel_Processor, } output_modes = { 'gab': 'classification', 'ws': 'classification', 'nyt': 'classification' } 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: torch.cuda.set_device(args.local_rank) 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') logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.info( "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}". format(device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(args.gradient_accumulation_steps)) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps 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_eval: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") #if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: # raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) # save configs f = open(os.path.join(args.output_dir, 'args.json'), 'w') json.dump(args.__dict__, f, indent=4) f.close() task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) processor = processors[task_name](configs, tokenizer=tokenizer) output_mode = output_modes[task_name] label_list = processor.get_labels() num_labels = len(label_list) train_examples = None num_train_optimization_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_optimization_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs if args.local_rank != -1: num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size( ) # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join( str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format( args.local_rank)) if args.do_train: model = BertForSequenceClassification.from_pretrained( args.bert_model, cache_dir=cache_dir, num_labels=num_labels) else: model = BertForSequenceClassification.from_pretrained( args.output_dir, num_labels=num_labels) model.to(device) if args.fp16: model.half() if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) model = DDP(model) # elif n_gpu > 1: # model = torch.nn.DataParallel(model) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training." ) optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) warmup_linear = WarmupLinearSchedule( warmup=args.warmup_proportion, t_total=num_train_optimization_steps) else: if args.do_train: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) global_step = 0 nb_tr_steps = 0 tr_loss, tr_reg_loss = 0, 0 tr_reg_cnt = 0 epoch = -1 val_best_f1 = -1 val_best_loss = 1e10 early_stop_countdown = args.early_stop if args.reg_explanations: train_lm_dataloder = processor.get_dataloader('train', configs.train_batch_size) dev_lm_dataloader = processor.get_dataloader('dev', configs.train_batch_size) explainer = SamplingAndOcclusionExplain( model, configs, tokenizer, device=device, vocab=tokenizer.vocab, train_dataloader=train_lm_dataloder, dev_dataloader=dev_lm_dataloader, lm_dir=args.lm_dir, output_path=os.path.join(configs.output_dir, configs.output_filename), ) else: explainer = None if args.do_train: epoch = 0 train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer, output_mode, configs) logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) if output_mode == "classification": all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) elif output_mode == "regression": all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) class_weight = torch.FloatTensor([args.negative_weight, 1]).to(device) model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate( tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch # define a new function to compute loss values for both output_modes logits = model(input_ids, segment_ids, input_mask, labels=None) if output_mode == "classification": loss_fct = CrossEntropyLoss(class_weight) loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1)) elif output_mode == "regression": loss_fct = MSELoss() loss = loss_fct(logits.view(-1), label_ids.view(-1)) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps tr_loss += loss.item() if args.fp16: optimizer.backward(loss) else: loss.backward() # regularize explanations # NOTE: backward performed inside this function to prevent OOM if args.reg_explanations: reg_loss, reg_cnt = explainer.compute_explanation_loss( input_ids, input_mask, segment_ids, label_ids, do_backprop=True) tr_reg_loss += reg_loss # float tr_reg_cnt += reg_cnt nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically lr_this_step = args.learning_rate * warmup_linear.get_lr( global_step / num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 if global_step % args.validate_steps == 0: val_result = validate(args, model, processor, tokenizer, output_mode, label_list, device, num_labels, task_name, tr_loss, global_step, epoch, explainer) val_acc, val_f1 = val_result['acc'], val_result['f1'] if val_f1 > val_best_f1: val_best_f1 = val_f1 if args.local_rank == -1 or torch.distributed.get_rank( ) == 0: save_model(args, model, tokenizer, num_labels) else: # halve the learning rate for param_group in optimizer.param_groups: param_group['lr'] *= 0.5 early_stop_countdown -= 1 logger.info( "Reducing learning rate... Early stop countdown %d" % early_stop_countdown) if early_stop_countdown < 0: break if early_stop_countdown < 0: break epoch += 1 # training finish ############################ # if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # if not args.explain: # args.test = True # validate(args, model, processor, tokenizer, output_mode, label_list, device, num_labels, # task_name, tr_loss, global_step=0, epoch=-1, explainer=explainer) # else: # args.test = True # explain(args, model, processor, tokenizer, output_mode, label_list, device) if not args.explain: args.test = True print('--Test_args.test: %s' % str(args.test)) #Test_args.test: True validate(args, model, processor, tokenizer, output_mode, label_list, device, num_labels, task_name, tr_loss, global_step=888, epoch=-1, explainer=explainer) args.test = False else: print('--Test_args.test: %s' % str(args.test)) # Test_args.test: True args.test = True explain(args, model, processor, tokenizer, output_mode, label_list, device) args.test = False