# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """BERT finetuning runner.""" from __future__ import absolute_import, division, print_function import argparse import csv import logging import os import random import sys import numpy as np import torch from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset) from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from torch.nn import CrossEntropyLoss, MSELoss from scipy.stats import pearsonr, spearmanr from sklearn.metrics import matthews_corrcoef, f1_score from sklearn import metrics from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig from pytorch_pretrained_bert.tokenization import BertTokenizer from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule os.environ['CUDA_VISIBLE_DEVICES']= '1' #torch.backends.cudnn.deterministic = True logger = logging.getLogger(__name__) class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, text_c=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. text_c: (Optional) string. The untokenized text of the third sequence. Only must be specified for sequence triple tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.text_c = text_c self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self, data_dir): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" with open(input_file, "r", encoding="utf-8") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: if sys.version_info[0] == 2: line = list(unicode(cell, 'utf-8') for cell in line) lines.append(line) return lines class KGProcessor(DataProcessor): """Processor for knowledge graph data set.""" def __init__(self): self.labels = set() def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", data_dir) def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev", data_dir) def get_test_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "test.tsv")), "test", data_dir) def get_relations(self, data_dir): """Gets all labels (relations) in the knowledge graph.""" # return list(self.labels) with open(os.path.join(data_dir, "relations.txt"), 'r') as f: lines = f.readlines() relations = [] for line in lines: relations.append(line.strip()) return relations def get_labels(self, data_dir): """Gets all labels (0, 1) for triples in the knowledge graph.""" return ["0", "1"] def get_entities(self, data_dir): """Gets all entities in the knowledge graph.""" # return list(self.labels) with open(os.path.join(data_dir, "entities.txt"), 'r') as f: lines = f.readlines() entities = [] for line in lines: entities.append(line.strip()) return entities def get_train_triples(self, data_dir): """Gets training triples.""" return self._read_tsv(os.path.join(data_dir, "train.tsv")) def get_dev_triples(self, data_dir): """Gets validation triples.""" return self._read_tsv(os.path.join(data_dir, "dev.tsv")) def get_test_triples(self, data_dir): """Gets test triples.""" return self._read_tsv(os.path.join(data_dir, "test.tsv")) def _create_examples(self, lines, set_type, data_dir): """Creates examples for the training and dev sets.""" # entity to text ent2text = {} with open(os.path.join(data_dir, "entity2text.txt"), 'r') as f: ent_lines = f.readlines() for line in ent_lines: temp = line.strip().split('\t') if len(temp) == 2: end = temp[1]#.find(',') ent2text[temp[0]] = temp[1]#[:end] if data_dir.find("FB15") != -1: with open(os.path.join(data_dir, "entity2textlong.txt"), 'r') as f: ent_lines = f.readlines() for line in ent_lines: temp = line.strip().split('\t') #first_sent_end_position = temp[1].find(".") ent2text[temp[0]] = temp[1]#[:first_sent_end_position + 1] entities = list(ent2text.keys()) rel2text = {} with open(os.path.join(data_dir, "relation2text.txt"), 'r') as f: rel_lines = f.readlines() for line in rel_lines: temp = line.strip().split('\t') rel2text[temp[0]] = temp[1] lines_str_set = set(['\t'.join(line) for line in lines]) examples = [] for (i, line) in enumerate(lines): head_ent_text = ent2text[line[0]] tail_ent_text = ent2text[line[2]] relation_text = rel2text[line[1]] if set_type == "dev" or set_type == "test": label = "1" guid = "%s-%s" % (set_type, i) text_a = head_ent_text text_b = relation_text text_c = tail_ent_text self.labels.add(label) examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c = text_c, label=label)) elif set_type == "train": guid = "%s-%s" % (set_type, i) text_a = head_ent_text text_b = relation_text text_c = tail_ent_text examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c = text_c, label="1")) rnd = random.random() guid = "%s-%s" % (set_type + "_corrupt", i) if rnd <= 0.5: # corrupting head for j in range(5): tmp_head = '' while True: tmp_ent_list = set(entities) tmp_ent_list.remove(line[0]) tmp_ent_list = list(tmp_ent_list) tmp_head = random.choice(tmp_ent_list) tmp_triple_str = tmp_head + '\t' + line[1] + '\t' + line[2] if tmp_triple_str not in lines_str_set: break tmp_head_text = ent2text[tmp_head] examples.append( InputExample(guid=guid, text_a=tmp_head_text, text_b=text_b, text_c = text_c, label="0")) else: # corrupting tail tmp_tail = '' for j in range(5): while True: tmp_ent_list = set(entities) tmp_ent_list.remove(line[2]) tmp_ent_list = list(tmp_ent_list) tmp_tail = random.choice(tmp_ent_list) tmp_triple_str = line[0] + '\t' + line[1] + '\t' + tmp_tail if tmp_triple_str not in lines_str_set: break tmp_tail_text = ent2text[tmp_tail] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c = tmp_tail_text, label="0")) return examples def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, print_info = True): """Loads a data file into a list of `InputBatch`s.""" label_map = {label : i for i, label in enumerate(label_list)} features = [] for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0 and print_info: logger.info("Writing example %d of %d" % (ex_index, len(examples))) tokens_a = tokenizer.tokenize(example.text_a) tokens_b = None tokens_c = None if example.text_b and example.text_c: tokens_b = tokenizer.tokenize(example.text_b) tokens_c = tokenizer.tokenize(example.text_c) # Modifies `tokens_a`, `tokens_b` and `tokens_c`in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP], [SEP] with "- 4" #_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_seq_length - 4) else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: tokens_a = tokens_a[:(max_seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 # (c) for sequence triples: # tokens: [CLS] Steve Jobs [SEP] founded [SEP] Apple Inc .[SEP] # type_ids: 0 0 0 0 1 1 0 0 0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence or the third sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambiguously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. tokens = ["[CLS]"] + tokens_a + ["[SEP]"] segment_ids = [0] * len(tokens) if tokens_b: tokens += tokens_b + ["[SEP]"] segment_ids += [1] * (len(tokens_b) + 1) if tokens_c: tokens += tokens_c + ["[SEP]"] segment_ids += [0] * (len(tokens_c) + 1) input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. padding = [0] * (max_seq_length - len(input_ids)) input_ids += padding input_mask += padding segment_ids += padding assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length label_id = label_map[example.label] if ex_index < 5 and print_info: logger.info("*** Example ***") logger.info("guid: %s" % (example.guid)) logger.info("tokens: %s" % " ".join( [str(x) for x in tokens])) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) logger.info( "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) logger.info("label: %s (id = %d)" % (example.label, label_id)) features.append( InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id)) return features def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length): """Truncates a sequence triple in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) + len(tokens_c) if total_length <= max_length: break if len(tokens_a) > len(tokens_b) and len(tokens_a) > len(tokens_c): tokens_a.pop() elif len(tokens_b) > len(tokens_a) and len(tokens_b) > len(tokens_c): tokens_b.pop() elif len(tokens_c) > len(tokens_a) and len(tokens_c) > len(tokens_b): tokens_c.pop() else: tokens_c.pop() def simple_accuracy(preds, labels): return (preds == labels).mean() def compute_metrics(task_name, preds, labels): assert len(preds) == len(labels) if task_name == "kg": return {"acc": simple_accuracy(preds, labels)} else: raise KeyError(task_name) def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.") parser.add_argument("--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters parser.add_argument("--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3") parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the test set.") parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', type=float, default=0, help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() processors = { "kg": KGProcessor, } if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( args.gradient_accumulation_steps)) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps args.seed = random.randint(1, 200) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) task_name = args.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(args.data_dir) num_labels = len(label_list) entity_list = processor.get_entities(args.data_dir) #print(entity_list) tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None num_train_optimization_steps = 0 if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_optimization_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs if args.local_rank != -1: num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)) model = BertForSequenceClassification.from_pretrained(args.bert_model, cache_dir=cache_dir, num_labels=num_labels) if args.fp16: model.half() model.to(device) if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) #model = torch.nn.parallel.data_parallel(model) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion, t_total=num_train_optimization_steps) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) global_step = 0 nb_tr_steps = 0 tr_loss = 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_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) 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 = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) model.train() #print(model) for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch # define a new function to compute loss values for both output_modes logits = model(input_ids, segment_ids, input_mask, labels=None) #print(logits, logits.shape) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1)) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: optimizer.backward(loss) else: 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: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step/num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 print("Training loss: ", tr_loss, nb_tr_examples) if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Save a trained model, configuration and tokenizer model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self # If we save using the predefined names, we can load using `from_pretrained` output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) torch.save(model_to_save.state_dict(), output_model_file) model_to_save.config.to_json_file(output_config_file) tokenizer.save_vocabulary(args.output_dir) # Load a trained model and vocabulary that you have fine-tuned model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels) tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) else: model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels) model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): 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) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) # Load a trained model and vocabulary that you have fine-tuned model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels) tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(device) model.eval() eval_loss = 0 nb_eval_steps = 0 preds = [] for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): 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 = model(input_ids, segment_ids, input_mask, labels=None) # create eval loss and other metric required by the task loss_fct = CrossEntropyLoss() tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1)) print(label_ids.view(-1)) eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if len(preds) == 0: preds.append(logits.detach().cpu().numpy()) else: preds[0] = np.append( preds[0], logits.detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps preds = preds[0] preds = np.argmax(preds, axis=1) result = compute_metrics(task_name, preds, all_label_ids.numpy()) loss = tr_loss/nb_tr_steps if args.do_train else None result['eval_loss'] = eval_loss result['global_step'] = global_step result['loss'] = loss 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]))) if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): train_triples = processor.get_train_triples(args.data_dir) dev_triples = processor.get_dev_triples(args.data_dir) test_triples = processor.get_test_triples(args.data_dir) all_triples = train_triples + dev_triples + test_triples all_triples_str_set = set() for triple in all_triples: triple_str = '\t'.join(triple) all_triples_str_set.add(triple_str) eval_examples = processor.get_test_examples(args.data_dir) eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running Prediction *****") 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) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) # Load a trained model and vocabulary that you have fine-tuned model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels) tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(device) model.eval() eval_loss = 0 nb_eval_steps = 0 preds = [] for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Testing"): 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 = model(input_ids, segment_ids, input_mask, labels=None) loss_fct = CrossEntropyLoss() tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1)) eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if len(preds) == 0: preds.append(logits.detach().cpu().numpy()) else: preds[0] = np.append( preds[0], logits.detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps preds = preds[0] print(preds, preds.shape) all_label_ids = all_label_ids.numpy() preds = np.argmax(preds, axis=1) result = compute_metrics(task_name, preds, all_label_ids) loss = tr_loss/nb_tr_steps if args.do_train else None result['eval_loss'] = eval_loss result['global_step'] = global_step result['loss'] = loss output_eval_file = os.path.join(args.output_dir, "test_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Test results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) print("Triple classification acc is : ") print(metrics.accuracy_score(all_label_ids, preds)) # run link prediction ranks = [] ranks_left = [] ranks_right = [] hits_left = [] hits_right = [] hits = [] top_ten_hit_count = 0 for i in range(10): hits_left.append([]) hits_right.append([]) hits.append([]) ''' file_prefix = str(args.data_dir[7:]) f = open(file_prefix + '_ranks.txt','r') lines = f.readlines() for line in lines: temp = line.strip().split() rank1 = int(temp[0]) ranks_left.append(rank1+1) print('left: ', rank1) ranks.append(rank1+1) if rank1 < 10: top_ten_hit_count += 1 rank2 = int(temp[1]) ranks.append(rank2+1) ranks_right.append(rank2+1) print('right: ', rank2) print('mean rank until now: ', np.mean(ranks)) if rank2 < 10: top_ten_hit_count += 1 print("hit@10 until now: ", top_ten_hit_count * 1.0 / len(ranks)) for hits_level in range(10): if rank1 <= hits_level: hits[hits_level].append(1.0) hits_left[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_left[hits_level].append(0.0) if rank2 <= hits_level: hits[hits_level].append(1.0) hits_right[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_right[hits_level].append(0.0) ''' for test_triple in test_triples: head = test_triple[0] relation = test_triple[1] tail = test_triple[2] #print(test_triple, head, relation, tail) head_corrupt_list = [test_triple] for corrupt_ent in entity_list: if corrupt_ent != head: tmp_triple = [corrupt_ent, relation, tail] tmp_triple_str = '\t'.join(tmp_triple) if tmp_triple_str not in all_triples_str_set: # may be slow head_corrupt_list.append(tmp_triple) tmp_examples = processor._create_examples(head_corrupt_list, "test", args.data_dir) print(len(tmp_examples)) tmp_features = convert_examples_to_features(tmp_examples, label_list, args.max_seq_length, tokenizer, print_info = False) all_input_ids = torch.tensor([f.input_ids for f in tmp_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in tmp_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in tmp_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in tmp_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for temp data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() preds = [] for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Testing"): 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 = model(input_ids, segment_ids, input_mask, labels=None) if len(preds) == 0: batch_logits = logits.detach().cpu().numpy() preds.append(batch_logits) else: batch_logits = logits.detach().cpu().numpy() preds[0] = np.append(preds[0], batch_logits, axis=0) preds = preds[0] # get the dimension corresponding to current label 1 #print(preds, preds.shape) rel_values = preds[:, all_label_ids[0]] rel_values = torch.tensor(rel_values) #print(rel_values, rel_values.shape) _, argsort1 = torch.sort(rel_values, descending=True) #print(max_values) #print(argsort1) argsort1 = argsort1.cpu().numpy() rank1 = np.where(argsort1 == 0)[0][0] print('left: ', rank1) ranks.append(rank1+1) ranks_left.append(rank1+1) if rank1 < 10: top_ten_hit_count += 1 tail_corrupt_list = [test_triple] for corrupt_ent in entity_list: if corrupt_ent != tail: tmp_triple = [head, relation, corrupt_ent] tmp_triple_str = '\t'.join(tmp_triple) if tmp_triple_str not in all_triples_str_set: # may be slow tail_corrupt_list.append(tmp_triple) tmp_examples = processor._create_examples(tail_corrupt_list, "test", args.data_dir) #print(len(tmp_examples)) tmp_features = convert_examples_to_features(tmp_examples, label_list, args.max_seq_length, tokenizer, print_info = False) all_input_ids = torch.tensor([f.input_ids for f in tmp_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in tmp_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in tmp_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in tmp_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for temp data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() preds = [] for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Testing"): 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 = model(input_ids, segment_ids, input_mask, labels=None) if len(preds) == 0: batch_logits = logits.detach().cpu().numpy() preds.append(batch_logits) else: batch_logits = logits.detach().cpu().numpy() preds[0] = np.append(preds[0], batch_logits, axis=0) preds = preds[0] # get the dimension corresponding to current label 1 rel_values = preds[:, all_label_ids[0]] rel_values = torch.tensor(rel_values) _, argsort1 = torch.sort(rel_values, descending=True) argsort1 = argsort1.cpu().numpy() rank2 = np.where(argsort1 == 0)[0][0] ranks.append(rank2+1) ranks_right.append(rank2+1) print('right: ', rank2) print('mean rank until now: ', np.mean(ranks)) if rank2 < 10: top_ten_hit_count += 1 print("hit@10 until now: ", top_ten_hit_count * 1.0 / len(ranks)) file_prefix = str(args.data_dir[7:]) + "_" + str(args.train_batch_size) + "_" + str(args.learning_rate) + "_" + str(args.max_seq_length) + "_" + str(args.num_train_epochs) #file_prefix = str(args.data_dir[7:]) f = open(file_prefix + '_ranks.txt','a') f.write(str(rank1) + '\t' + str(rank2) + '\n') f.close() # this could be done more elegantly, but here you go for hits_level in range(10): if rank1 <= hits_level: hits[hits_level].append(1.0) hits_left[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_left[hits_level].append(0.0) if rank2 <= hits_level: hits[hits_level].append(1.0) hits_right[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_right[hits_level].append(0.0) for i in [0,2,9]: logger.info('Hits left @{0}: {1}'.format(i+1, np.mean(hits_left[i]))) logger.info('Hits right @{0}: {1}'.format(i+1, np.mean(hits_right[i]))) logger.info('Hits @{0}: {1}'.format(i+1, np.mean(hits[i]))) logger.info('Mean rank left: {0}'.format(np.mean(ranks_left))) logger.info('Mean rank right: {0}'.format(np.mean(ranks_right))) logger.info('Mean rank: {0}'.format(np.mean(ranks))) logger.info('Mean reciprocal rank left: {0}'.format(np.mean(1./np.array(ranks_left)))) logger.info('Mean reciprocal rank right: {0}'.format(np.mean(1./np.array(ranks_right)))) logger.info('Mean reciprocal rank: {0}'.format(np.mean(1./np.array(ranks)))) if __name__ == "__main__": main()