def __init__(self): processors = { "Discourage": DiscourageProcessor, "DiscourageTest": DiscourageProcessor, "DiscourageMask": DiscourageProcessor } self.processor = processors[config.task_name]() self.tokenizer = tokenization.FullTokenizer( vocab_file=config.vocab_file, do_lower_case=config.do_lower_case) self.label_path = os.path.join(config.data_dir, "args.bin") self.train_path = os.path.join(config.data_dir, "train.bin") self.dev_path = os.path.join(config.data_dir, "dev.bin") self.test_path = os.path.join(config.data_dir, "test.bin") self.train_examples = None self.num_train_steps = None if not os.path.exists(self.label_path): self.train_examples = self.processor.get_train_examples( config.data_dir) label_list = self.processor.get_labels() self.fw = open(self.label_path, "wb+") pickle.dump(label_list, self.fw, -1) pickle.dump(len(self.train_examples), self.fw, -1) # self.fw.write(str(label_list)) self.labels_list = label_list self.num_train_steps = len(self.train_examples) else: self.fw = open(self.label_path, "rb") self.labels_list = pickle.load(self.fw) self.num_train_steps = pickle.load(self.fw) self.fw.close() self.test_data_loader = None self.train_data_loader = None pass
def __init__(self, opt): print("load data ...") self.opt = opt self.train_examples = opt.processor.get_train_examples(opt.data_dir) self.eval_examples = opt.processor.get_dev_examples(opt.data_dir) self.label_list = opt.processor.get_labels() self.tokenizer = tokenization.FullTokenizer( vocab_file=opt.vocab_file, do_lower_case=opt.do_lower_case) self.train_dataloader = self.get_data_loader( examples=self.train_examples, type='train_data') self.eval_dataloader = self.get_data_loader( examples=self.eval_examples, type='eval_data')
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_config_file", default=None, type=str, required=True, help= "The config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture.") parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.") parser.add_argument( "--vocab_file", default=None, type=str, required=True, help="The vocabulary file that the BERT model was trained on.") 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( "--init_checkpoint", default=None, type=str, help="Initial checkpoint (usually from a pre-trained BERT model).") parser.add_argument( "--do_lower_case", default=False, action='store_true', help= "Whether to lower case the input text. True for uncased models, False for cased models." ) 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("--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("--save_checkpoints_steps", default=1000, type=int, help="How often to save the model checkpoint.") 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 accumualte before performing a backward/update pass." ) 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.' ) args = parser.parse_args() processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, "news": NewsProcessor, "car": CarProcessor, } 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 %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) 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.") bert_config = BertConfig.from_json_file(args.bert_config_file) if args.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}" .format(args.max_seq_length, bert_config.max_position_embeddings)) if os.path.exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError( "Output directory ({}) already exists and is not empty.".format( args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) label_list = processor.get_labels() print("label_list.size:%d\n" % (len(label_list))) # Prepare model model = BertForSequenceClassification(bert_config, len(label_list)) if args.init_checkpoint is not None: model.bert.load_state_dict( torch.load(args.init_checkpoint, map_location='cpu')) 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: param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01 }, { 'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0 }] 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(train_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) 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 = RandomSampler(train_data) # train_sampler = DistributedSampler(train_data) train_data_loader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) 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_data_loader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch loss, _ = model(input_ids, segment_ids, input_mask, label_ids) 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() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 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(): 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() model.zero_grad() global_step += 1 torch.save(model.state_dict(), os.path.join(args.output_dir, "model.pkl")) if args.do_eval: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: eval_sampler = SequentialSampler(eval_data) else: eval_sampler = SequentialSampler(eval_data) # eval_sampler = DistributedSampler(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 for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) 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 result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': tr_loss / nb_tr_steps } output_eval_file = os.path.join(args.output_dir, "eval_results.txt") 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 save_pkl(filename, content): file = open('./pkl/' + filename, 'wb') pickle.dump(content, file) file.close() def load_pkl(filename): with open('./pkl/' + filename, 'rb') as file: return pickle.load(file) processor = NewsProcessor() predict_examples = processor.get_predict_examples(args.data_dir) label_list = load_pkl('label_list.pkl') tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) eval_features = convert_examples_to_features(predict_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) # model.load_state_dict(torch.load('./model/model_1.pkl', map_location='cpu')) # model = torch.load('./model/model_5_0.9772.pkl') model = torch.load('./model/model_9_0.9353.pkl') #model_14_0.9784.pkl model.eval() device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
def main(): os.environ['CUDA_VISIBLE_DEVICES'] = '0' os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' root_path = r'./' bert_path = './chinese_L-12_H-768_A-12' flags = tf.flags flags.DEFINE_string("data_dir", os.path.join(root_path, 'data'), "The input datadir.", ) flags.DEFINE_string("bert_config_file", os.path.join(bert_path, 'bert_config.json'), "The config json file corresponding to the pre-trained BERT model.") flags.DEFINE_string("task_name", 'ner', "The name of the task to train.") flags.DEFINE_string("vocab_file", os.path.join(bert_path, 'vocab.txt'), "The vocabulary file that the BERT model was trained on.") flags.DEFINE_string("output_dir", os.path.join(root_path, 'model'), "The output directory where the model checkpoints will be written.") ## Other parameters flags.DEFINE_string("init_checkpoint", os.path.join(bert_path, 'pytorch_model.bin'), "Initial checkpoint (usually from a pre-trained BERT model).") flags.DEFINE_bool("do_lower_case", True, "Whether to lower case the input text.") flags.DEFINE_integer("max_seq_length", 48, "The maximum total input sequence length after WordPiece tokenization.") # flags.DEFINE_boolean('clean', True, 'remove the files which created by last training') flags.DEFINE_bool("do_train", True, "Whether to run training.") flags.DEFINE_bool("do_eval", True, "Whether to run eval on the dev set.") flags.DEFINE_bool("no_cuda", False, "Whether not to use CUDA when available") # flags.DEFINE_bool("do_predict", True, "Whether to run the model in inference mode on the test set.") flags.DEFINE_integer("train_batch_size", 64, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 64, "Total batch size for eval.") # flags.DEFINE_integer("predict_batch_size", 4, "Total batch size for predict.") flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("save_model_epoch", 1, "save model ") flags.DEFINE_float("num_train_epochs", 15.0, "Total number of training epochs to perform.") flags.DEFINE_float('droupout_rate', 0.5, 'Dropout rate') flags.DEFINE_float('clip', 5, 'Gradient clip') flags.DEFINE_float("warmup_proportion", 0.1, "Proportion of training to perform linear learning rate warmup for. ""E.g., 0.1 = 10% of training.") flags.DEFINE_integer("save_checkpoints_steps", 50, "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 50, "How many steps to make in each estimator call.") flags.DEFINE_integer("local_rank", -1, "local_rank for distributed training on gpus") flags.DEFINE_integer("seed", 1, "random seed for initialization") flags.DEFINE_integer("gradient_accumulation_steps", 1, "Number of updates steps to accumualte before performing a backward/update pass.") flags.DEFINE_bool("optimize_on_cpu", False, "Whether to perform optimization and keep the optimizer averages on CPU") flags.DEFINE_bool("fp16", False, "Whether to use 16-bit float precision instead of 32-bit") flags.DEFINE_float('loss_scale', 128.0, 'Loss scaling, positive power of 2 values can improve fp16 convergence.') args = flags.FLAGS 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 %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) 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.") bert_config = BertConfig.from_json_file(args.bert_config_file) if args.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format( args.max_seq_length, bert_config.max_position_embeddings)) if os.path.exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) task_name = args.task_name.lower() processor = NewsProcessor() tokenizer = tokenization.FullTokenizer( vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) if os.path.exists('./pkl/label_list.pkl'): label_list = load_pkl('label_list.pkl') if args.do_train and os.path.exists('./pkl/label_list.pkl') == False: label_list = processor.get_labels() save_pkl('label_list.pkl', label_list) print("label_list.size:%d\n" % (len(label_list))) # Prepare model model = BertForSequenceClassification(bert_config, len(label_list)) if args.init_checkpoint is not None: model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu')) 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: param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01}, {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0} ] 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: def get_dev_result(): eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) 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 for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_accuracy = eval_accuracy / nb_eval_examples return eval_accuracy,eval_loss train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) 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 = RandomSampler(train_data) # train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) model.train() epoched = 1 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 loss, _ = model(input_ids, segment_ids, input_mask, label_ids) 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() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 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(): 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() model.zero_grad() global_step += 1 acc,loss = get_dev_result() acc = round(acc,4) def write_file(filename, str): """ 写入文件 :param str: 字符串 :return: 无 """ writefile = open("./eval/" + filename, 'a+', encoding='utf-8') writefile.write(str + '\n') writefile.close() write_file('log.txt',str(epoched)+' acc: --> '+str(acc)) write_file('log.txt', str(epoched) + ' loss: --> ' + str(loss)) write_file('log.txt', '===========================') print('dev acc:', acc) print('loss',loss) if int(epoched)>2: if int(epoched) % int(args.save_model_epoch) == 0: # torch.save(model.state_dict(), args.output_dir + '/model_{}.pkl'.format(str(epoched))) torch.save(model, args.output_dir + '/model_{}.pkl'.format(str(epoched) + '_' + str(acc))) if int(epoched) == int(args.num_train_epochs): torch.save(model, args.output_dir + '/model_end.pkl') print('test acc:', acc) print('dev', loss) epoched += 1 if args.do_eval: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) if args.do_train == False: # model.load_state_dict(torch.load('./model/model_1.pkl', map_location='cpu')) model = torch.load('./model/model_end.pkl') model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) 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 result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, } output_eval_file = os.path.join("./eval", "eval_results.txt") 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(args): processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, "news": NewsProcessor, } 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 %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) 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.") # bert_config = BertConfig.from_json_file(args.bert_config_file) bert_config = BertConfig(args.vocab_size) if args.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}" .format(args.max_seq_length, bert_config.max_position_embeddings)) if os.path.exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError( "Output directory ({}) already exists and is not empty.".format( args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) label_list = processor.get_labels() print("label_list.size:%d\n" % (len(label_list))) # Prepare model model = BertForSequenceClassification(bert_config, len(label_list)) if args.init_checkpoint is not None: model.bert.load_state_dict( torch.load(args.init_checkpoint, map_location='cpu')) 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: param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01 }, { 'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0 }] 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(train_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) 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 = RandomSampler(train_data) # train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) model.train() ### 状态设置 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 loss, _ = model(input_ids, segment_ids, input_mask, label_ids) 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() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 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(): 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() model.zero_grad() global_step += 1 if args.do_eval: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: eval_sampler = SequentialSampler(eval_data) else: eval_sampler = SequentialSampler(eval_data) # eval_sampler = DistributedSampler(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 for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) 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 result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': tr_loss / nb_tr_steps } output_eval_file = os.path.join(args.output_dir, "eval_results.txt") 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]))) torch.save(model.state_dict(), args.output_dir + '/bert_' + args.task_name + 'pt')
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=r"C:\workspace\python-work\nlp\nlp_framework\nlp-tutorial-ec\bert-Chinese-classification-task-ec\glue_data\data", 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_config_file", default=r"C:\workspace\python-work\nlp\nlp_framework\nlp-tutorial-ec\bert-Chinese-classification-task-ec\config\bert_config.json", type=str, # required=True, help="The config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture.") parser.add_argument("--task_name", default="NEWS", type=str, # required=True, help="The name of the task to train.") parser.add_argument("--vocab_file", default=r"C:\workspace\python-work\nlp\nlp_framework\nlp-tutorial-ec\bert-Chinese-classification-task-ec\config\vocab.txt", type=str, # required=True, help="The vocabulary file that the BERT model was trained on.") parser.add_argument("--output_dir", default="./newsAll_output/", type=str, # required=True, help="The output directory where the model checkpoints will be written.") ## Other parameters parser.add_argument("--init_checkpoint", default=r"D:\database\bert\chinese_L-12_H-768_A-12\pytorch_model.bin", type=str, help="Initial checkpoint (usually from a pre-trained BERT model).") parser.add_argument("--do_lower_case", default=False, action='store_true', help="Whether to lower case the input text. True for uncased models, False for cased models.") parser.add_argument("--max_seq_length", default=256, 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=True, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", default=True, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default=8, 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=2e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=1.0, 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("--save_checkpoints_steps", default=1000, type=int, help="How often to save the model checkpoint.") parser.add_argument("--no_cuda", default=True, 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 accumualte before performing a backward/update pass.") 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.') args = parser.parse_args() # opt=get_config(vars(args)) print("args---------->", args, "\n") processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, "news": NewsProcessor, } 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") device = torch.device("cpu") n_gpu = 0 # n_gpu = torch.cuda.device_count() print("device", device) 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 %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) 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.") bert_config = BertConfig.from_json_file(args.bert_config_file) if args.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format( args.max_seq_length, bert_config.max_position_embeddings)) if os.path.exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) # processor = NewsProcessor() processor = processors[task_name]() # print("tmp-----",processor) tokenizer = tokenization.FullTokenizer( vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None if args.do_train: # train_examples: examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) # NewsProcessor.get_train_examples(args.data_dir) train_examples = processor.get_train_examples(args.data_dir) # 300/8/1 * 1 = 37 num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) label_list = processor.get_labels() # <class 'list'>: ['houseliving', 'game', 'fashion'] print("label_list.size:%d\n" % (len(label_list))) # ------------------------------------important----------------------------------------- # ------------------------------------important----------------------------------------- # ------------------------------------important----------------------------------------- # ------------------------------------important----------------------------------------- # model # Prepare model #label_list <class 'list'>: ['fashion', 'game', 'houseliving'] model = BertForSequenceClassification(bert_config, len(label_list)) #3 # print(model) ''' BertForSequenceClassification( (bert): BertModel( (embeddings): BERTEmbeddings( (word_embeddings): Embedding(21128, 768) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): BERTLayerNorm() (dropout): Dropout(p=0.1) ) (encoder): BERTEncoder( (layer): ModuleList( (0-11): BERTLayer( (attention): BERTAttention( (self): BERTSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1) ) (output): BERTSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): BERTLayerNorm() (dropout): Dropout(p=0.1) ) ) (intermediate): BERTIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) ) (output): BERTOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): BERTLayerNorm() (dropout): Dropout(p=0.1) ) ) ) ) (pooler): BERTPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (dropout): Dropout(p=0.1) (classifier): Linear(in_features=768, out_features=3, bias=True) ) ''' # BertModel loading trained parameters if args.init_checkpoint is not None: model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu')) 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: # loaded parameter ----------->list[len:12layer] name:parameter param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] # len(optimizer_grouped_parameters)=2 # optimizer_grouped_parameters[0]:dict-----> optimizer_grouped_parameters[0]['weight_decay_rate'] =0.01 optimizer_grouped_parameters = [ # for name,parameter inparm_optimizer if name not in no_dacay {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01}, {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0} ] 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: #这个操作中没有masked language model 只有token——embedding 和segment——embedding # train_features: features.append( InputFeatures( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id)) train_features = convert_examples_to_features(train_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) 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 = RandomSampler(train_data) # 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"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): if(step<1): # print(2,type(batch)) #list batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch ####################################################################################\ # -----------------------training--------------------------------------- # -----------------------training--------------------------------------- # model 里面有torch.nn.CrossEntropyLoss(), 返回loss loss, _ = model(input_ids, segment_ids, input_mask, label_ids) 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() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) #=b=8 nb_tr_steps += 1 #0+1=1 if (step + 1) % args.gradient_accumulation_steps == 0: #gradient_accumulation_steps=1 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(): 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() model.zero_grad() global_step += 1 if args.do_eval: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer) 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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: eval_sampler = SequentialSampler(eval_data) else: eval_sampler = SequentialSampler(eval_data) # eval_sampler = DistributedSampler(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 for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): #logits: (b,output_size)=(8,3) 表示最后一个encode输出的(b,m,d)中的firt token ---->(b,d)---------->(b,ouputsize) tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) #每一个batch预测对的个数=4 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 #所有次数loss的平均值 eval_accuracy = eval_accuracy / nb_eval_examples #预测对的/所有的预测数目 result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': tr_loss / nb_tr_steps} #training中的loss平均值 output_eval_file = os.path.join(args.output_dir, "eval_results.txt") #./newsAll_output/ eval_results.txt 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(_): tf.logging.set_verbosity(tf.logging.INFO) processors = { "news": NewsProcessor, "cola": ColaProcessor, "mnli": MnliProcessor, "mrpc": MrpcProcessor, "xnli": XnliProcessor, } #tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, # FLAGS.init_checkpoint) if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict: raise ValueError( "At least one of `do_train`, `do_eval` or `do_predict' must be True." ) bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) if FLAGS.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (FLAGS.max_seq_length, bert_config.max_position_embeddings)) tf.gfile.MakeDirs(FLAGS.output_dir) task_name = FLAGS.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) model_fn = model_fn_builder(bert_config=bert_config, num_labels=len(label_list), init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size, predict_batch_size=FLAGS.predict_batch_size) if FLAGS.do_train: train_file = os.path.join(FLAGS.output_dir, "train.tf_record") file_based_convert_examples_to_features(train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) tf.logging.info("***** Running training *****") tf.logging.info(" Num examples = %d", len(train_examples)) tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.logging.info(" Num steps = %d", num_train_steps) train_input_fn = file_based_input_fn_builder( input_file=train_file, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) if FLAGS.do_eval: eval_examples = processor.get_dev_examples(FLAGS.data_dir) num_actual_eval_examples = len(eval_examples) if FLAGS.use_tpu: # TPU requires a fixed batch size for all batches, therefore the number # of examples must be a multiple of the batch size, or else examples # will get dropped. So we pad with fake examples which are ignored # later on. These do NOT count towards the metric (all tf.metrics # support a per-instance weight, and these get a weight of 0.0). while len(eval_examples) % FLAGS.eval_batch_size != 0: eval_examples.append(PaddingInputExample()) eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record") file_based_convert_examples_to_features(eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Num examples = %d (%d actual, %d padding)", len(eval_examples), num_actual_eval_examples, len(eval_examples) - num_actual_eval_examples) tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) # This tells the estimator to run through the entire set. eval_steps = None # However, if running eval on the TPU, you will need to specify the # number of steps. if FLAGS.use_tpu: assert len(eval_examples) % FLAGS.eval_batch_size == 0 eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size) eval_drop_remainder = True if FLAGS.use_tpu else False eval_input_fn = file_based_input_fn_builder( input_file=eval_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if FLAGS.do_predict: predict_examples = processor.get_test_examples(FLAGS.data_dir) num_actual_predict_examples = len(predict_examples) if FLAGS.use_tpu: # TPU requires a fixed batch size for all batches, therefore the number # of examples must be a multiple of the batch size, or else examples # will get dropped. So we pad with fake examples which are ignored # later on. while len(predict_examples) % FLAGS.predict_batch_size != 0: predict_examples.append(PaddingInputExample()) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") file_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file) tf.logging.info("***** Running prediction*****") tf.logging.info(" Num examples = %d (%d actual, %d padding)", len(predict_examples), num_actual_predict_examples, len(predict_examples) - num_actual_predict_examples) tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) predict_drop_remainder = True if FLAGS.use_tpu else False predict_input_fn = file_based_input_fn_builder( input_file=predict_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) result = estimator.predict(input_fn=predict_input_fn) output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") with tf.gfile.GFile(output_predict_file, "w") as writer: num_written_lines = 0 tf.logging.info("***** Predict results *****") for (i, prediction) in enumerate(result): probabilities = prediction["probabilities"] if i >= num_actual_predict_examples: break output_line = "\t".join( str(class_probability) for class_probability in probabilities) + "\n" writer.write(output_line) num_written_lines += 1 assert num_written_lines == num_actual_predict_examples