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("--ernie_model", default=None, type=str, required=True, help="Ernie pre-trained model") parser.add_argument( "--output_dir", default=None, type=str, required=True, help= "The output directory where the model predictions and checkpoints will be written." ) ## Other parameters 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", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument( "--do_lower_case", default=False, 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("--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", default=False, 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', 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=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('--threshold', type=float, default=.3) args = parser.parse_args() 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') 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 = int(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)) os.makedirs(args.output_dir, exist_ok=True) processor = TypingProcessor() tokenizer_label = BertTokenizer_label.from_pretrained( args.ernie_model, do_lower_case=args.do_lower_case) tokenizer = BertTokenizer.from_pretrained(args.ernie_model, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None train_examples, label_list, d = processor.get_train_examples(args.data_dir) label_list = sorted(label_list) #class_weight = [min(d[x], 100) for x in label_list] #logger.info(class_weight) S = [] for l in label_list: s = [] for ll in label_list: if ll in l: s.append(1.) else: s.append(0.) S.append(s) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) # Prepare model model, _ = BertForEntityTyping.from_pretrained( args.ernie_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank), num_labels=len(label_list)) if args.fp16: model.half() model.to(device) 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_grad = [ 'bert.encoder.layer.11.output.dense_ent', 'bert.encoder.layer.11.output.LayerNorm_ent' ] param_optimizer = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_grad)] 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 }] t_total = num_train_steps if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() 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) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=t_total) global_step = 0 vecs = [] vecs.append([0] * 100) with open("kg_embed/entity2vec.vec", 'r') as fin: for line in fin: vec = line.strip().split('\t') vec = [float(x) for x in vec] vecs.append(vec) embed = torch.FloatTensor(vecs) embed = torch.nn.Embedding.from_pretrained(embed) logger.info("Shape of entity embedding: " + str(embed.weight.size())) del vecs if args.do_train: train_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer_label, tokenizer, args.threshold) 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_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) all_input_ent = torch.tensor([f.input_ent for f in train_features], dtype=torch.long) all_ent_mask = torch.tensor([f.ent_mask for f in train_features], dtype=torch.long) all_labels = torch.tensor([f.labels for f in train_features], dtype=torch.float) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_input_ent, all_ent_mask, all_labels) 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) output_loss_file = os.path.join(args.output_dir, "loss") loss_fout = open(output_loss_file, 'w') model.train() for epoch 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) if i != 3 else t for i, t in enumerate(batch)) input_ids, input_mask, segment_ids, input_ent, ent_mask, labels = batch input_ent = embed(input_ent + 1).to(device) loss = model(input_ids, segment_ids, input_mask, input_ent.half(), ent_mask, labels.half()) #loss = model(input_ids, segment_ids, input_mask, input_ent, ent_mask, labels) 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 if args.fp16: optimizer.backward(loss) else: loss.backward() loss_fout.write("{}\n".format( loss.item() * args.gradient_accumulation_steps)) tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: # modify learning rate with special warm up BERT uses lr_this_step = args.learning_rate * warmup_linear( global_step / t_total, 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 % 150 == 0 and global_step > 0: model_to_save = model.module if hasattr( model, 'module') else model output_model_file = os.path.join( args.output_dir, "pytorch_model.bin_{}".format(global_step)) torch.save(model_to_save.state_dict(), output_model_file) model_to_save = model.module if hasattr(model, 'module') else model output_model_file = os.path.join( args.output_dir, "pytorch_model.bin_{}".format(epoch)) torch.save(model_to_save.state_dict(), output_model_file) exit(0)
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("--ernie_model", default=None, type=str, required=True, help="Ernie pre-trained model") 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("--model_name_or_path", default='/data1', type=str) ## Other parameters 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", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument( "--do_lower_case", default=False, 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=8, type=int, help="Total batch size for eval.") 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", default=False, 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', 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=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('--threshold', type=float, default=.3) args = parser.parse_args() 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') 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)) 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) processor = TypingProcessor() tokenizer_label = BertTokenizer_label.from_pretrained( args.ernie_model, do_lower_case=args.do_lower_case) tokenizer = BertTokenizer.from_pretrained(args.ernie_model, do_lower_case=args.do_lower_case) _, label_list, _ = processor.get_train_examples(args.data_dir) label_list = sorted(label_list) #class_weight = [min(d[x], 100) for x in label_list] #logger.info(class_weight) # S = [] # for l in label_list: # s = [] # for ll in label_list: # if ll in l: # s.append(1.) # else: # s.append(0.) # S.append(s) # vecs = [] # vecs.append([0]*100) # with open("kg_embed/entity2vec.vec", 'r') as fin: # for line in fin: # vec = line.strip().split('\t') # vec = [float(x) for x in vec] # vecs.append(vec) # embed = torch.FloatTensor(vecs) # embed = torch.nn.Embedding.from_pretrained(embed) # logger.info("Shape of entity embedding: "+str(embed.weight.size())) # del vecs filenames = os.listdir(args.output_dir) filenames = [x for x in filenames if "pytorch_model.bin_" in x] file_mark = [] for x in filenames: file_mark.append([x, True]) file_mark.append([x, False]) for x, mark in file_mark: print(x, mark) output_model_file = os.path.join(args.output_dir, x) model_state_dict = torch.load(output_model_file) bert_model = BertModel.from_pretrained(args.model_name_or_path) model = BertForEntityTyping(bert_model, len(label_list)) model.load_state_dict(model_state_dict) #model, _ = BertForEntityTyping.from_pretrained(args.ernie_model, state_dict=model_state_dict, num_labels=len(label_list)) model.to(device) if mark: eval_examples = processor.get_dev_examples(args.data_dir) else: eval_examples = processor.get_test_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer_label, tokenizer, args.threshold) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) # zeros = [0 for _ in range(args.max_seq_length)] # zeros_ent = [0 for _ in range(100)] # zeros_ent = [zeros_ent for _ in range(args.max_seq_length)] 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_span_mask = torch.tensor([f.span_mask for f in eval_features], dtype=torch.float) all_labels = torch.tensor([f.labels for f in eval_features], dtype=torch.float) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_span_mask, all_labels) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 pred = [] true = [] for input_ids, input_mask, segment_ids, span_mask, labels in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) span_mask = span_mask.to(device) labels = labels.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, span_mask, labels) logits = model(input_ids, segment_ids, input_mask, span_mask) logits = logits.detach().cpu().numpy() labels = labels.to('cpu').numpy() tmp_eval_accuracy, tmp_pred, tmp_true = accuracy(logits, labels) pred.extend(tmp_pred) true.extend(tmp_true) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples def f1(p, r): if r == 0.: return 0. return 2 * p * r / float(p + r) def loose_macro(true, pred): num_entities = len(true) p = 0. r = 0. for true_labels, predicted_labels in zip(true, pred): if len(predicted_labels) > 0: p += len( set(predicted_labels).intersection( set(true_labels))) / float(len(predicted_labels)) if len(true_labels): r += len( set(predicted_labels).intersection( set(true_labels))) / float(len(true_labels)) precision = p / num_entities recall = r / num_entities return precision, recall, f1(precision, recall) def loose_micro(true, pred): num_predicted_labels = 0. num_true_labels = 0. num_correct_labels = 0. for true_labels, predicted_labels in zip(true, pred): num_predicted_labels += len(predicted_labels) num_true_labels += len(true_labels) num_correct_labels += len( set(predicted_labels).intersection(set(true_labels))) if num_predicted_labels > 0: precision = num_correct_labels / num_predicted_labels else: precision = 0. recall = num_correct_labels / num_true_labels return precision, recall, f1(precision, recall) result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'macro': loose_macro(true, pred), 'micro': loose_micro(true, pred) } if mark: output_eval_file = os.path.join( args.output_dir, "eval_results_{}.txt".format(x.split("_")[-1])) else: output_eval_file = os.path.join( args.output_dir, "test_results_{}.txt".format(x.split("_")[-1])) with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key])))
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("--train_file", default=None, type=str, required=True) parser.add_argument("--ernie_model", default=None, type=str, required=True, help="Ernie pre-trained model") 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("--ckpt", default='None', type=str) ## Other parameters 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", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument( "--do_lower_case", default=False, 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("--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("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") 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", default=False, 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', 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=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('--mean_pool', type=float, default=1) parser.add_argument("--bert_model", type=str, default='bert') args = parser.parse_args() logger.info(args) print(args) 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') 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 = int(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)) os.makedirs(args.output_dir, exist_ok=True) processor = TypingProcessor() tokenizer_label = BertTokenizer_label.from_pretrained( args.ernie_model, do_lower_case=args.do_lower_case) tokenizer = BertTokenizer.from_pretrained(args.ernie_model, do_lower_case=args.do_lower_case) if os.path.exists('***path_to_your_roberta***'): load_path = '***path_to_your_roberta***' else: load_path = '***path_to_your_roberta***' roberta_tokenizer = RobertaTokenizer.from_pretrained(load_path) bert_tokenizer_cased = BertTokenizer_cased.from_pretrained( '***path_to_your_bert_tokenizer_cased***') train_examples = None num_train_steps = None train_examples, label_list, d = processor.get_train_examples( args.data_dir, args.train_file) label_list = sorted(label_list) #class_weight = [min(d[x], 100) for x in label_list] #logger.info(class_weight) S = [] for l in label_list: s = [] for ll in label_list: if ll in l: s.append(1.) else: s.append(0.) S.append(s) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) # Prepare model if args.bert_model == 'bert' and args.do_lower_case: if os.path.exists('***path_to_your_bert_uncased***'): bert_model = BertModel.from_pretrained( '***path_to_your_bert_uncased***') else: bert_model = BertModel.from_pretrained( '***path_to_your_bert_uncased***') if args.ckpt != 'None': if os.path.exists('***path_to_your_bert_uncased***'): load_path = '***path_to_your_trained_checkpoint***' + args.ckpt else: load_path = '***path_to_your_trained_checkpoint***' + args.ckpt ckpt = torch.load(load_path) bert_model.load_state_dict(ckpt["bert-base"]) elif args.bert_model == 'roberta': if os.path.exists('***path_to_your_roberta***'): bert_model = RobertaModel.from_pretrained( '***path_to_your_roberta***') else: bert_model = RobertaModel.from_pretrained( '***path_to_your_roberta***') if args.ckpt != 'None': if os.path.exists('***path_to_your_roberta***'): load_path = '***path_to_your_trained_checkpoint***' + args.ckpt else: load_path = '***path_to_your_trained_checkpoint***' + args.ckpt ckpt = torch.load(load_path) bert_model.load_state_dict(ckpt["bert-base"]) else: bert_model = BertModel.from_pretrained( '***path_to_your_bert_model_cased***') model = BertForEntityTyping(bert_model, len(label_list)) if args.fp16: model.half() model.to(device) 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_grad = [ 'bert.encoder.layer.11.output.dense_ent', 'bert.encoder.layer.11.output.LayerNorm_ent' ] param_optimizer = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_grad)] 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 }] t_total = num_train_steps if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() 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) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=t_total) global_step = 0 if args.do_train: if args.do_lower_case: if args.train_file == 'train.json' and os.path.exists( 'train_features_1.0' ) and 'FIGER' in args.data_dir and args.mean_pool == 1: train_features = torch.load('train_features_1.0') elif args.train_file == 'train.json' and os.path.exists( 'train_features_1.0_se' ) and 'FIGER' in args.data_dir and args.mean_pool == 0: train_features = torch.load('train_features_1.0_se') else: train_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer_label, tokenizer, roberta_tokenizer, bert_tokenizer_cased, args.mean_pool, args.bert_model, args.do_lower_case) else: if args.train_file == 'train.json' and os.path.exists( 'train_features_1.0' ) and 'FIGER' in args.data_dir and args.mean_pool == 1: train_features = torch.load('train_features_cased') else: train_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer_label, tokenizer, roberta_tokenizer, bert_tokenizer_cased, args.mean_pool, args.bert_model, args.do_lower_case) 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_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) all_span_mask = torch.tensor([f.span_mask for f in train_features], dtype=torch.float) all_labels = torch.tensor([f.labels for f in train_features], dtype=torch.float) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_span_mask, all_labels) 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) output_loss_file = os.path.join(args.output_dir, "loss") loss_fout = open(output_loss_file, 'w') model.train() for epoch 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(train_dataloader): batch = tuple( t.to(device) if i != 3 else t for i, t in enumerate(batch)) input_ids, input_mask, segment_ids, span_mask, labels = batch loss = model(input_ids, args.bert_model, segment_ids, input_mask, span_mask, labels.half()) 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 if args.fp16: optimizer.backward(loss) else: loss.backward() loss_fout.write("{}\n".format( loss.item() * args.gradient_accumulation_steps)) tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: # modify learning rate with special warm up BERT uses lr_this_step = args.learning_rate * warmup_linear( global_step / t_total, 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 % 150 == 0 and global_step > 0: model_to_save = model.module if hasattr( model, 'module') else model output_model_file = os.path.join( args.output_dir, "pytorch_model.bin_{}".format(global_step)) torch.save(model_to_save.state_dict(), output_model_file) model_to_save = model.module if hasattr(model, 'module') else model output_model_file = os.path.join( args.output_dir, "pytorch_model.bin_{}".format(epoch)) torch.save(model_to_save.state_dict(), output_model_file) x = "pytorch_model.bin_{}".format(epoch) for mark in [True, False]: if mark: eval_examples = processor.get_dev_examples(args.data_dir) else: eval_examples = processor.get_test_examples(args.data_dir) eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer_label, tokenizer, roberta_tokenizer, bert_tokenizer_cased, args.mean_pool, args.bert_model, args.do_lower_case) 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) all_span_mask = torch.tensor( [f.span_mask for f in eval_features], dtype=torch.float) all_labels = torch.tensor([f.labels for f in eval_features], dtype=torch.float) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_span_mask, all_labels) eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 pred = [] true = [] for input_ids, input_mask, segment_ids, span_mask, labels in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) span_mask = span_mask.to(device) labels = labels.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, args.bert_model, segment_ids, input_mask, span_mask, labels) logits = model(input_ids, args.bert_model, segment_ids, input_mask, span_mask) logits = logits.detach().cpu().numpy() labels = labels.to('cpu').numpy() tmp_eval_accuracy, tmp_pred, tmp_true = accuracy( logits, labels) pred.extend(tmp_pred) true.extend(tmp_true) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples def f1(p, r): if r == 0.: return 0. return 2 * p * r / float(p + r) def loose_macro(true, pred): num_entities = len(true) p = 0. r = 0. for true_labels, predicted_labels in zip(true, pred): if len(predicted_labels) > 0: p += len( set(predicted_labels).intersection( set(true_labels))) / float( len(predicted_labels)) if len(true_labels): r += len( set(predicted_labels).intersection( set(true_labels))) / float( len(true_labels)) precision = p / num_entities recall = r / num_entities return precision, recall, f1(precision, recall) def loose_micro(true, pred): num_predicted_labels = 0. num_true_labels = 0. num_correct_labels = 0. for true_labels, predicted_labels in zip(true, pred): num_predicted_labels += len(predicted_labels) num_true_labels += len(true_labels) num_correct_labels += len( set(predicted_labels).intersection( set(true_labels))) if num_predicted_labels > 0: precision = num_correct_labels / num_predicted_labels else: precision = 0. recall = num_correct_labels / num_true_labels return precision, recall, f1(precision, recall) if False: result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'macro': loose_macro(true, pred), 'micro': loose_micro(true, pred) } else: result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'macro': loose_macro(true, pred), 'micro': loose_micro(true, pred) } if mark: output_eval_file = os.path.join( args.output_dir, "eval_results_{}.txt".format(x.split("_")[-1])) else: output_eval_file = os.path.join( args.output_dir, "test_results_{}.txt".format(x.split("_")[-1])) with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) exit(0)