def prepare_data(self): "Called to initialize data. Use the call to construct features" args = self.hparams for mode in ["train", "dev", "test"]: cached_features_file = self._feature_file(mode) if not os.path.exists(cached_features_file): logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) features = convert_examples_to_features( examples, self.labels, args.max_seq_length, self.tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), cls_token=self.tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=self.tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), pad_on_left=bool(args.model_type in ["xlnet"]), pad_token=self.tokenizer.convert_tokens_to_ids( [self.tokenizer.pad_token])[0], pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0, pad_token_label_id=self.pad_token_label_id, ) logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file)
def load_and_cache_examples(tokenizer, labels): examples = read_examples_from_file('./', mode='test') features = convert_examples_to_features( examples, labels, 128, tokenizer, cls_token=tokenizer.cls_token, cls_token_segment_id=0, sep_token=tokenizer.sep_token, pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0]) # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset
def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}".format( mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length)), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args.model_type in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.pad_token_id, pad_token_segment_id=tokenizer.pad_token_type_id, pad_token_label_id=pad_token_label_id, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset
def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}".format( mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length))) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=False, cls_token=tokenizer.cls_token, cls_token_segment_id=0, sep_token=tokenizer.sep_token, sep_token_extra=False, pad_on_left=False, pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token ])[0], pad_token_segment_id=0, pad_token_label_id=pad_token_label_id) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset
def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, batch_size, mode): drop_remainder = True if args["tpu"] or mode == "train" else False # Load data features from cache or dataset file cached_features_file = os.path.join( args["data_dir"], "cached_{}_{}_{}.tf_record".format( mode, list(filter(None, args["model_name_or_path"].split("/"))).pop(), str(args["max_seq_length"])), ) if os.path.exists(cached_features_file) and not args["overwrite_cache"]: logging.info("Loading features from cached file %s", cached_features_file) dataset, size = load_cache(cached_features_file, args["max_seq_length"]) else: logging.info("Creating features from dataset file at %s", args["data_dir"]) examples = read_examples_from_file(args["data_dir"], mode) features = convert_examples_to_features( examples, labels, args["max_seq_length"], tokenizer, cls_token_at_end=bool(args["model_type"] in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args["model_type"] in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args["model_type"] in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args["model_type"] in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token ])[0], pad_token_segment_id=4 if args["model_type"] in ["xlnet"] else 0, pad_token_label_id=pad_token_label_id, ) logging.info("Saving features into cached file %s", cached_features_file) save_cache(features, cached_features_file) dataset, size = load_cache(cached_features_file, args["max_seq_length"]) if mode == "train": dataset = dataset.repeat() dataset = dataset.shuffle(buffer_size=8192, seed=args["seed"]) dataset = dataset.batch(batch_size, drop_remainder) dataset = dataset.prefetch(buffer_size=batch_size) return dataset, size
def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, lang, mode): data_path = os.path.join(args.data_dir, lang) # Load data features from cache or dataset file cached_features_file = os.path.join(data_path, "cached_{}_{}_{}".format(mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length))) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s.", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", data_path) examples = read_examples_from_file(data_path, mode) features = convert_examples_to_features(examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=False, # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=0, sep_token=tokenizer.sep_token, sep_token_extra=False, # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=False, # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], pad_token_segment_id=0, pad_token_label_id=pad_token_label_id ) logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset
def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""): #eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # multi-gpu evaluate if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running evaluation %s *****", prefix) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None model.eval() if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) print(len(examples)) print(examples[0]) # list of words in one document (sentence) out_label_listX=[] preds_listX=[] for example in tqdm(examples, desc="Evaluating"): max_length = 500 min_context = 128 l = len(example.words) #TODO number of segments for each word? word_tokens = tokenizer.tokenize(word) word_tokens_lengths=[len(tokenizer.tokenize(word)) for word in example.words] ws=windows(word_tokens_lengths, max_length, min_context) print(ws) text_examples=[] for start_all, start_content, end_content, end_all in ws: ex=InputExample(guid=example.guid, words=example.words[start_all:end_all], labels=example.labels[start_all:end_all]) text_examples.append(ex) # przed tym trzeba podzielić features = convert_examples_to_features( text_examples, labels, 512, #args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args.model_type in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0, pad_token_label_id=pad_token_label_id, ) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) eval_dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # Eval! logger.info(" Num examples = %d", len(eval_dataset)) # batch = next(iter(eval_dataloader)) a = [] b = [] for batch, (start_all, start_content, end_content, end_all) in tqdm(zip(eval_dataloader, ws), desc="Evaluating"): preds = None out_label_ids = None batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] if args.n_gpu > 1: tmp_eval_loss = tmp_eval_loss.mean() # mean() to average on multi-gpu parallel evaluating eval_loss += tmp_eval_loss.item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) preds = np.argmax(preds, axis=2) label_map = {i: label for i, label in enumerate(labels)} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] for i in range(out_label_ids.shape[0]): for j in range(out_label_ids.shape[1]): if out_label_ids[i, j] != pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) #join for i in range(len(out_label_list)): a.extend(out_label_list[i][start_content-start_all:end_content-start_all]) b.extend(preds_list[i][start_content-start_all:end_content-start_all]) out_label_listX.append(a) preds_listX.append(b) # results = { # "loss": eval_loss, # "precision": precision_score(out_label_list, preds_list), # "recall": recall_score(out_label_list, preds_list), # "f1": f1_score(out_label_list, preds_list), # } eval_loss = eval_loss / nb_eval_steps try: results = { "loss": eval_loss, "precision": precision_score(out_label_listX, preds_listX), "recall": recall_score(out_label_listX, preds_listX), "f1": f1_score(out_label_listX, preds_listX), } logger.info("***** Eval results %s *****", prefix) for key in sorted(results.keys()): logger.info(" %s = %s", key, str(results[key])) return results, preds_listX except IndexError: #no output labels in file return {}, preds_listX
num_labels=num_labels, id2label={str(i): label for i, label in enumerate(labels)}, label2id=label2id, cache_dir=args.cache_dir if args.cache_dir else None, ) model = AutoModelForTokenClassification.from_pretrained( args.model_name_or_path) model.load_state_dict( torch.load(os.path.join(args.model_name_or_path, 'pytorch_model.bin'), map_location='cpu')) model.to(device) pad_token_label_id = CrossEntropyLoss().ignore_index examples = read_examples_from_file(args.data_dir, mode) features, all_tokens = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), pad_on_left=bool(args.model_type in ["xlnet"]), pad_token=tokenizer.pad_token_id, pad_token_segment_id=tokenizer.pad_token_type_id, pad_token_label_id=pad_token_label_id,
model_dir = 'model/' model = model_class.from_pretrained(model_dir) case = False tokenizer = tokenizer_class.from_pretrained(model_dir, do_lower_case=case) device = 'cpu' model.to(device) # setting the folder for possible whole directory tagging files_dir = 'data/' # getting labels labels = get_labels('labels.txt') pad_token_label_id = CrossEntropyLoss().ignore_index # reading examples examples = read_examples_from_file('.', 'test') features = convert_examples_to_features( examples, labels, 256, tokenizer, cls_token_at_end=False, cls_token=tokenizer.cls_token, cls_token_segment_id=0, sep_token=tokenizer.sep_token, sep_token_extra=False, pad_on_left=False, pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], pad_token_segment_id=0, pad_token_label_id=pad_token_label_id) all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, lang, mode, plain_text=False): data_path = os.path.join(args.data_dir, lang) # Load data features from cache or dataset file cached_features_file = os.path.join( data_path, "cached_{}_{}_{}".format( mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length))) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s. Plain text: %s", cached_features_file, plain_text) features = torch.load(cached_features_file) else: logger.info( "Creating features from dataset file at %s. Plain text: %s", data_path, plain_text) examples = read_examples_from_file(data_path, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=False, # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=0, sep_token=tokenizer.sep_token, sep_token_extra=False, pad_on_left=False, # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token ])[0], pad_token_segment_id=0, pad_token_label_id=pad_token_label_id) logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) if not plain_text: all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset else: # assert lang in args.src_langs and lang != args.tgt_lang language_id = args.src_langs.index( lang) if lang in args.src_langs else len(args.src_langs) all_language_id = torch.tensor([language_id] * len(features), dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_language_id) return dataset
def load_and_cache_examples(args, task, tokenizer, labels, pad_token_label_id, mode): args.data_dir = args.base_data_dir + task.lower() try: # print("old args.data_dir: ", args.data_dir, flush=True) args.data_dir = args.data_dir.replace('/home/rizwan/.flair/datasets/', '/local/rizwan/UDTree/') # print("new args.data_dir: ", args.data_dir, flush=True) if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}".format( mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length)), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info('-' * 120) logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) logger.info('-' * 120) else: logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args.model_type in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids( [tokenizer.pad_token])[0], pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0, pad_token_label_id=pad_token_label_id, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Data Shepley debug if args.data_size and mode == "train": if args.data_size < len(features): logger.info('-' * 50) logger.info( f'originial data size: {len(features)} truncated to {args.data_size}' ) features = np.random.choice(features, args.data_size, replace=False) logger.info('-' * 50) # Convert to Tensors and build dataset logger.info("- Creating Tensors") all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) logger.info("- Creating Tensor dataset") dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) random_init_result = None return dataset, random_init_result, all_label_ids.shape[0] except: print("could not print args.data_dir", flush=True) if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}".format( mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length)), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info('-' * 120) logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) logger.info('-' * 120) else: logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args.model_type in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids( [tokenizer.pad_token])[0], pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0, pad_token_label_id=pad_token_label_id, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier( ) # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Data Shepley debug if args.data_size and mode == "train": if args.data_size < len(features): logger.info('-' * 50) logger.info( f'originial data size: {len(features)} truncated to {args.data_size}' ) features = np.random.choice(features, args.data_size, replace=False) logger.info('-' * 50) # Convert to Tensors and build dataset logger.info("- Creating Tensors") all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) logger.info("- Creating Tensor dataset") dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) random_init_result = None return dataset, random_init_result, all_label_ids.shape[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 files named train, dev, and test.") parser.add_argument("--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) 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( "--sample_type", default="conll", type=str, help= "raw sample type, conll for standard two column style, cluener for json type" ) parser.add_argument( "--labels", default="", type=str, help= "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name") parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name") parser.add_argument( "--cache_dir", default="", type=str, help= "Where do you want to store the pre-trained models downloaded from s3") parser.add_argument( "--max_seq_length", default=128, type=int, help= "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.") parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Whether to run evaluation during training at each logging step.") parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.") parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.") 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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--max_steps", default=-1, type=int, help= "If > 0: set total number of training steps to perform. Override num_train_epochs." ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help= "Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number" ) parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument("--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory") parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets") parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help= "Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help= "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html") parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if os.path.exists(args.output_dir) and os.listdir( args.output_dir ) and args.do_train and not args.overwrite_output_dir: raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome." .format(args.output_dir)) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training 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") args.n_gpu = torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16) # Set seed set_seed(args) # Prepare CONLL-2003 task labels = get_labels(args.labels) num_labels = len(labels) # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later pad_token_label_id = CrossEntropyLoss().ignore_index # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier( ) # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config) if args.local_rank == 0: torch.distributed.barrier( ) # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train") global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = model.module if hasattr( model, "module") else model # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained( args.output_dir, do_lower_case=args.do_lower_case) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted( glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))) logging.getLogger("pytorch_transformers.modeling_utils").setLevel( logging.WARN) # Reduce logging logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split( "-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", prefix=global_step) if global_step: result = { "{}_{}".format(global_step, k): v for k, v in result.items() } results.update(result) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(results.keys()): writer.write("{} = {}\n".format(key, str(results[key]))) if args.do_predict and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained( args.output_dir, do_lower_case=args.do_lower_case) model = model_class.from_pretrained(args.output_dir) model.to(args.device) result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test") # Save results output_test_results_file = os.path.join(args.output_dir, "test_results.txt") with open(output_test_results_file, "w") as writer: for key in sorted(result.keys()): writer.write("{} = {}\n".format(key, str(result[key]))) # Save predictions output_test_predictions_file = os.path.join(args.output_dir, "test_predictions.txt") with open(output_test_predictions_file, "w") as writer: if args.sample_type == 'conll': testdataset = read_examples_from_file(args.data_dir, 'test') elif args.sample_type == 'cluener': testdataset = read_cluener_example_from_file( args.data_dir, 'test') example_id = 0 for data in testdataset: for word in data.words: if predictions[example_id]: output_line = word + " " + predictions[example_id].pop( 0) + "\n" writer.write(output_line) else: logger.warning( "Maximum sequence length exceeded: No prediction for '%s'.", word) example_id += 1 return results