import torch import torch.nn as nn from torch.nn import functional as F from torch.optim import Adam, SGD from torch.utils.data import DataLoader import pickle from bert import BERT from seq_model import BERTSM from classifier_model import BERTForClassification from optim_schedule import ScheduledOptim import tqdm import sys import numpy as np import visualization from sklearn.metrics import precision_score, recall_score, f1_score class ECE(nn.Module): def __init__(self, n_bins=15): """ n_bins (int): number of confidence interval bins """ super(ECE, self).__init__() bin_boundaries = torch.linspace(0, 1, n_bins + 1) self.bin_lowers = bin_boundaries[:-1] self.bin_uppers = bin_boundaries[1:] def forward(self, logits, labels): softmaxes = F.softmax(logits, dim=1) confidences, predictions = torch.max(softmaxes, 1) labels = torch.argmax(labels,1) accuracies = predictions.eq(labels) ece = torch.zeros(1, device=logits.device) for bin_lower, bin_upper in zip(self.bin_lowers, self.bin_uppers): # Calculated |confidence - accuracy| in each bin in_bin = confidences.gt(bin_lower.item()) * confidences.le(bin_upper.item()) prop_in_bin = in_bin.float().mean() if prop_in_bin.item() > 0: accuracy_in_bin = accuracies[in_bin].float().mean() avg_confidence_in_bin = confidences[in_bin].mean() ece += torch.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin return ece def accurate_nb(preds, labels): pred_flat = np.argmax(preds, axis=1).flatten() labels_flat = np.argmax(labels, axis=1).flatten() labels_flat = labels.flatten() return np.sum(pred_flat == labels_flat) class BERTTrainer: """ # Sequence.. BERTTrainer make the pretrained BERT model with two LM training method. 1. Masked Language Model : 3.3.1 Task #1: Masked LM """ def __init__(self, bert: BERT, vocab_size: int, train_dataloader: DataLoader, test_dataloader: DataLoader = None, lr: float = 1e-4, betas=(0.9, 0.999), weight_decay: float = 0.01, warmup_steps=10000, with_cuda: bool = True, cuda_devices=None, log_freq: int = 10, same_student_prediction = False, workspace_name=None): """ :param bert: BERT model which you want to train :param vocab_size: total word vocab size :param train_dataloader: train dataset data loader :param test_dataloader: test dataset data loader [can be None] :param lr: learning rate of optimizer :param betas: Adam optimizer betas :param weight_decay: Adam optimizer weight decay param :param with_cuda: traning with cuda :param log_freq: logging frequency of the batch iteration """ # Setup cuda device for BERT training, argument -c, --cuda should be true cuda_condition = torch.cuda.is_available() and with_cuda self.device = torch.device("cuda:0" if cuda_condition else "cpu") print("Device used = ", self.device) # This BERT model will be saved every epoch self.bert = bert # Initialize the BERT Language Model, with BERT model self.model = BERTSM(bert, vocab_size).to(self.device) # Distributed GPU training if CUDA can detect more than 1 GPU if with_cuda and torch.cuda.device_count() > 1: print("Using %d GPUS for BERT" % torch.cuda.device_count()) self.model = nn.DataParallel(self.model, device_ids=cuda_devices) # Setting the train and test data loader self.train_data = train_dataloader self.test_data = test_dataloader # Setting the Adam optimizer with hyper-param self.optim = Adam(self.model.parameters(), lr=lr, betas=betas, weight_decay=weight_decay) self.optim_schedule = ScheduledOptim(self.optim, self.bert.hidden, n_warmup_steps=warmup_steps) # Using Negative Log Likelihood Loss function for predicting the masked_token self.criterion = nn.NLLLoss(ignore_index=0) self.log_freq = log_freq self.same_student_prediction = same_student_prediction self.workspace_name = workspace_name self.save_model = False self.avg_loss = 10000 print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()])) def train(self, epoch): self.iteration(epoch, self.train_data) def test(self, epoch): self.iteration(epoch, self.test_data, train=False) def iteration(self, epoch, data_loader, train=True): """ loop over the data_loader for training or testing if on train status, backward operation is activated and also auto save the model every peoch :param epoch: current epoch index :param data_loader: torch.utils.data.DataLoader for iteration :param train: boolean value of is train or test :return: None """ str_code = "train" if train else "test" code = "masked_prediction" if self.same_student_prediction else "masked" self.log_file = f"{self.workspace_name}/logs/{code}/log_{str_code}_pretrained.txt" bert_hidden_representations = [] if epoch == 0: f = open(self.log_file, 'w') f.close() if not train: self.avg_loss = 10000 # Setting the tqdm progress bar data_iter = tqdm.tqdm(enumerate(data_loader), desc="EP_%s:%d" % (str_code, epoch), total=len(data_loader), bar_format="{l_bar}{r_bar}") avg_loss_mask = 0.0 total_correct_mask = 0 total_element_mask = 0 avg_loss_pred = 0.0 total_correct_pred = 0 total_element_pred = 0 avg_loss = 0.0 with open(self.log_file, 'a') as f: sys.stdout = f for i, data in data_iter: # 0. batch_data will be sent into the device(GPU or cpu) data = {key: value.to(self.device) for key, value in data.items()} # 1. forward the next_sentence_prediction and masked_lm model # next_sent_output, mask_lm_output = self.model.forward(data["bert_input"], data["segment_label"]) if self.same_student_prediction: bert_hidden_rep, mask_lm_output, same_student_output = self.model.forward(data["bert_input"], data["segment_label"], self.same_student_prediction) else: bert_hidden_rep, mask_lm_output = self.model.forward(data["bert_input"], data["segment_label"], self.same_student_prediction) embeddings = [h for h in bert_hidden_rep.cpu().detach().numpy()] bert_hidden_representations.extend(embeddings) # 2-2. NLLLoss of predicting masked token word mask_loss = self.criterion(mask_lm_output.transpose(1, 2), data["bert_label"]) # 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure if self.same_student_prediction: # 2-1. NLL(negative log likelihood) loss of is_next classification result same_student_loss = self.criterion(same_student_output, data["is_same_student"]) loss = same_student_loss + mask_loss else: loss = mask_loss # 3. backward and optimization only in train if train: self.optim_schedule.zero_grad() loss.backward() self.optim_schedule.step_and_update_lr() non_zero_mask = (data["bert_label"] != 0).float() predictions = torch.argmax(mask_lm_output, dim=-1) predicted_masked = predictions*non_zero_mask mask_correct = ((data["bert_label"] == predicted_masked)*non_zero_mask).sum().item() avg_loss_mask += loss.item() total_correct_mask += mask_correct total_element_mask += non_zero_mask.sum().item() post_fix = { "epoch": epoch, "iter": i, "avg_loss": avg_loss_mask / (i + 1), "avg_acc_mask": total_correct_mask / total_element_mask * 100, "loss": loss.item() } # next sentence prediction accuracy if self.same_student_prediction: correct = same_student_output.argmax(dim=-1).eq(data["is_same_student"]).sum().item() avg_loss_pred += loss.item() total_correct_pred += correct total_element_pred += data["is_same_student"].nelement() # correct = next_sent_output.argmax(dim=-1).eq(data["is_next"]).sum().item() post_fix["avg_loss"] = avg_loss_pred / (i + 1) post_fix["avg_acc_pred"] = total_correct_pred / total_element_pred * 100 post_fix["loss"] = loss.item() avg_loss +=loss.item() if i % self.log_freq == 0: data_iter.write(str(post_fix)) # if not train and epoch > 20 : # pickle.dump(mask_lm_output.cpu().detach().numpy(), open(f"logs/mask/mask_out_e{epoch}_{i}.pkl","wb")) # pickle.dump(data["bert_label"].cpu().detach().numpy(), open(f"logs/mask/label_e{epoch}_{i}.pkl","wb")) final_msg = { "epoch": f"EP{epoch}_{str_code}", "avg_loss": avg_loss / len(data_iter), "total_masked_acc": total_correct_mask * 100.0 / total_element_mask } if self.same_student_prediction: final_msg["total_prediction_acc"] = total_correct_pred * 100.0 / total_element_pred print(final_msg) # print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter), "total_masked_acc=", total_correct_mask * 100.0 / total_element_mask, "total_prediction_acc=", total_correct_pred * 100.0 / total_element_pred) # else: # print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter), "total_masked_acc=", total_correct_mask * 100.0 / total_element_mask) # print("EP%d_%s, " % (epoch, str_code)) f.close() sys.stdout = sys.__stdout__ self.save_model = False if self.avg_loss > (avg_loss / len(data_iter)): self.save_model = True self.avg_loss = (avg_loss / len(data_iter)) # pickle.dump(bert_hidden_representations, open(f"embeddings/{code}/{str_code}_embeddings_{epoch}.pkl","wb")) def save(self, epoch, file_path="output/bert_trained.model"): """ Saving the current BERT model on file_path :param epoch: current epoch number :param file_path: model output path which gonna be file_path+"ep%d" % epoch :return: final_output_path """ output_path = file_path + ".ep%d" % epoch torch.save(self.bert.cpu(), output_path) self.bert.to(self.device) print("EP:%d Model Saved on:" % epoch, output_path) return output_path class BERTFineTuneTrainer: def __init__(self, bert: BERT, vocab_size: int, train_dataloader: DataLoader, test_dataloader: DataLoader = None, lr: float = 1e-4, betas=(0.9, 0.999), weight_decay: float = 0.01, warmup_steps=10000, with_cuda: bool = True, cuda_devices=None, log_freq: int = 10, workspace_name=None, num_labels=2): """ :param bert: BERT model which you want to train :param vocab_size: total word vocab size :param train_dataloader: train dataset data loader :param test_dataloader: test dataset data loader [can be None] :param lr: learning rate of optimizer :param betas: Adam optimizer betas :param weight_decay: Adam optimizer weight decay param :param with_cuda: traning with cuda :param log_freq: logging frequency of the batch iteration """ # Setup cuda device for BERT training, argument -c, --cuda should be true cuda_condition = torch.cuda.is_available() and with_cuda self.device = torch.device("cuda:0" if cuda_condition else "cpu") print("Device used = ", self.device) # This BERT model will be saved every epoch self.bert = bert # for param in self.bert.parameters(): # param.requires_grad = False # Initialize the BERT Language Model, with BERT model self.model = BERTForClassification(self.bert, vocab_size, num_labels).to(self.device) # Distributed GPU training if CUDA can detect more than 1 GPU if with_cuda and torch.cuda.device_count() > 1: print("Using %d GPUS for BERT" % torch.cuda.device_count()) self.model = nn.DataParallel(self.model, device_ids=cuda_devices) # Setting the train and test data loader self.train_data = train_dataloader self.test_data = test_dataloader self.optim = Adam(self.model.parameters(), lr=lr, weight_decay=weight_decay, eps=1e-9) # self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=2, factor=0.1) if num_labels == 1: self.criterion = nn.MSELoss() elif num_labels == 2: self.criterion = nn.CrossEntropyLoss() elif num_labels > 2: self.criterion = nn.BCEWithLogitsLoss() self.ece_criterion = ECE().to(self.device) self.log_freq = log_freq self.workspace_name = workspace_name self.save_model = False self.avg_loss = 10000 print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()])) def train(self, epoch): self.iteration(epoch, self.train_data) def test(self, epoch): self.iteration(epoch, self.test_data, train=False) def iteration(self, epoch, data_loader, train=True): """ loop over the data_loader for training or testing if on train status, backward operation is activated and also auto save the model every peoch :param epoch: current epoch index :param data_loader: torch.utils.data.DataLoader for iteration :param train: boolean value of is train or test :return: None """ str_code = "train" if train else "test" self.log_file = f"{self.workspace_name}/logs/masked/log_{str_code}_FS_finetuned.txt" if epoch == 0: f = open(self.log_file, 'w') f.close() if not train: self.avg_loss = 10000 # Setting the tqdm progress bar data_iter = tqdm.tqdm(enumerate(data_loader), desc="EP_%s:%d" % (str_code, epoch), total=len(data_loader), bar_format="{l_bar}{r_bar}") avg_loss = 0.0 total_correct = 0 total_element = 0 plabels = [] tlabels = [] eval_accurate_nb = 0 nb_eval_examples = 0 logits_list = [] labels_list = [] if train: self.model.train() else: self.model.eval() with open(self.log_file, 'a') as f: sys.stdout = f for i, data in data_iter: # 0. batch_data will be sent into the device(GPU or cpu) data = {key: value.to(self.device) for key, value in data.items()} if train: h_rep, logits = self.model.forward(data["bert_input"], data["segment_label"]) else: with torch.no_grad(): h_rep, logits = self.model.forward(data["bert_input"], data["segment_label"]) # print(logits, logits.shape) logits_list.append(logits.cpu()) labels_list.append(data["progress_status"].cpu()) # print(">>>>>>>>>>>>", progress_output) # print(f"{epoch}---nelement--- {data['progress_status'].nelement()}") # print(data["progress_status"].shape, logits.shape) progress_loss = self.criterion(logits, data["progress_status"]) loss = progress_loss if torch.cuda.device_count() > 1: loss = loss.mean() # 3. backward and optimization only in train if train: self.optim.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) self.optim.step() # progress prediction accuracy # correct = progress_output.argmax(dim=-1).eq(data["progress_status"]).sum().item() probs = nn.LogSoftmax(dim=-1)(logits) predicted_labels = torch.argmax(probs, dim=-1) true_labels = torch.argmax(data["progress_status"], dim=-1) plabels.extend(predicted_labels.cpu().numpy()) tlabels.extend(true_labels.cpu().numpy()) # print(">>>>>>>>>>>>>>", predicted_labels, true_labels) # Compare predicted labels to true labels and calculate accuracy correct = (predicted_labels == true_labels).sum().item() avg_loss += loss.item() total_correct += correct total_element += true_labels.nelement() if train: post_fix = { "epoch": epoch, "iter": i, "avg_loss": avg_loss / (i + 1), "avg_acc": total_correct / total_element * 100, "loss": loss.item() } else: logits = logits.detach().cpu().numpy() label_ids = data["progress_status"].to('cpu').numpy() tmp_eval_nb = accurate_nb(logits, label_ids) eval_accurate_nb += tmp_eval_nb nb_eval_examples += label_ids.shape[0] total_element += data["progress_status"].nelement() # avg_loss += loss.item() post_fix = { "epoch": epoch, "iter": i, "avg_loss": avg_loss / (i + 1), "avg_acc": tmp_eval_nb / total_element * 100, "loss": loss.item() } if i % self.log_freq == 0: data_iter.write(str(post_fix)) # precisions = precision_score(plabels, tlabels, average="weighted") # recalls = recall_score(plabels, tlabels, average="weighted") f1_scores = f1_score(plabels, tlabels, average="weighted") if train: final_msg = { "epoch": f"EP{epoch}_{str_code}", "avg_loss": avg_loss / len(data_iter), "total_acc": total_correct * 100.0 / total_element, # "precisions": precisions, # "recalls": recalls, "f1_scores": f1_scores } else: eval_accuracy = eval_accurate_nb/nb_eval_examples logits_ece = torch.cat(logits_list) labels_ece = torch.cat(labels_list) ece = self.ece_criterion(logits_ece, labels_ece).item() final_msg = { "epoch": f"EP{epoch}_{str_code}", "eval_accuracy": eval_accuracy, "ece": ece, "avg_loss": avg_loss / len(data_iter), # "precisions": precisions, # "recalls": recalls, "f1_scores": f1_scores } if self.save_model: conf_hist = visualization.ConfidenceHistogram() plt_test = conf_hist.plot(np.array(logits_ece), np.array(labels_ece), title= f"Confidence Histogram {epoch}") plt_test.savefig(f"{self.workspace_name}/plots/confidence_histogram/FS/conf_histogram_test_{epoch}.png",bbox_inches='tight') plt_test.close() rel_diagram = visualization.ReliabilityDiagram() plt_test_2 = rel_diagram.plot(np.array(logits_ece), np.array(labels_ece),title=f"Reliability Diagram {epoch}") plt_test_2.savefig(f"{self.workspace_name}/plots/confidence_histogram/FS/rel_diagram_test_{epoch}.png",bbox_inches='tight') plt_test_2.close() print(final_msg) # print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter), "total_acc=", total_correct * 100.0 / total_element) f.close() sys.stdout = sys.__stdout__ if train: self.save_model = False if self.avg_loss > (avg_loss / len(data_iter)): self.save_model = True self.avg_loss = (avg_loss / len(data_iter)) # plt_test.show() # print("EP%d_%s, " % (epoch, str_code)) def save(self, epoch, file_path="output/bert_fine_tuned_trained.model"): """ Saving the current BERT model on file_path :param epoch: current epoch number :param file_path: model output path which gonna be file_path+"ep%d" % epoch :return: final_output_path """ output_path = file_path + ".ep%d" % epoch torch.save(self.model.cpu(), output_path) self.model.to(self.device) print("EP:%d Model Saved on:" % epoch, output_path) return output_path