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| # 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, unicode_literals | |
| import argparse | |
| import logging | |
| import os | |
| import random | |
| from io import open | |
| import numpy as np | |
| import torch | |
| from torch.utils.data import DataLoader, Dataset, RandomSampler | |
| from torch.utils.data.distributed import DistributedSampler | |
| from tqdm import tqdm, trange | |
| from pytorch_pretrained_bert.modeling import BertForPreTraining | |
| from pytorch_pretrained_bert.tokenization import BertTokenizer | |
| from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule | |
| logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', | |
| datefmt='%m/%d/%Y %H:%M:%S', | |
| level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| class BERTDataset(Dataset): | |
| def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True): | |
| self.vocab = tokenizer.vocab | |
| self.tokenizer = tokenizer | |
| self.seq_len = seq_len | |
| self.on_memory = on_memory | |
| self.corpus_lines = corpus_lines # number of non-empty lines in input corpus | |
| self.corpus_path = corpus_path | |
| self.encoding = encoding | |
| self.current_doc = 0 # to avoid random sentence from same doc | |
| # for loading samples directly from file | |
| self.sample_counter = 0 # used to keep track of full epochs on file | |
| self.line_buffer = None # keep second sentence of a pair in memory and use as first sentence in next pair | |
| # for loading samples in memory | |
| self.current_random_doc = 0 | |
| self.num_docs = 0 | |
| self.sample_to_doc = [] # map sample index to doc and line | |
| # load samples into memory | |
| if on_memory: | |
| self.all_docs = [] | |
| doc = [] | |
| self.corpus_lines = 0 | |
| with open(corpus_path, "r", encoding=encoding) as f: | |
| for line in tqdm(f, desc="Loading Dataset", total=corpus_lines): | |
| line = line.strip() | |
| if line == "": | |
| self.all_docs.append(doc) | |
| doc = [] | |
| #remove last added sample because there won't be a subsequent line anymore in the doc | |
| self.sample_to_doc.pop() | |
| else: | |
| #store as one sample | |
| sample = {"doc_id": len(self.all_docs), | |
| "line": len(doc)} | |
| self.sample_to_doc.append(sample) | |
| doc.append(line) | |
| self.corpus_lines = self.corpus_lines + 1 | |
| # if last row in file is not empty | |
| if self.all_docs[-1] != doc: | |
| self.all_docs.append(doc) | |
| self.sample_to_doc.pop() | |
| self.num_docs = len(self.all_docs) | |
| # load samples later lazily from disk | |
| else: | |
| if self.corpus_lines is None: | |
| with open(corpus_path, "r", encoding=encoding) as f: | |
| self.corpus_lines = 0 | |
| for line in tqdm(f, desc="Loading Dataset", total=corpus_lines): | |
| if line.strip() == "": | |
| self.num_docs += 1 | |
| else: | |
| self.corpus_lines += 1 | |
| # if doc does not end with empty line | |
| if line.strip() != "": | |
| self.num_docs += 1 | |
| self.file = open(corpus_path, "r", encoding=encoding) | |
| self.random_file = open(corpus_path, "r", encoding=encoding) | |
| def __len__(self): | |
| # last line of doc won't be used, because there's no "nextSentence". Additionally, we start counting at 0. | |
| return self.corpus_lines - self.num_docs - 1 | |
| def __getitem__(self, item): | |
| cur_id = self.sample_counter | |
| self.sample_counter += 1 | |
| if not self.on_memory: | |
| # after one epoch we start again from beginning of file | |
| if cur_id != 0 and (cur_id % len(self) == 0): | |
| self.file.close() | |
| self.file = open(self.corpus_path, "r", encoding=self.encoding) | |
| t1, t2, is_next_label = self.random_sent(item) | |
| # tokenize | |
| tokens_a = self.tokenizer.tokenize(t1) | |
| tokens_b = self.tokenizer.tokenize(t2) | |
| # combine to one sample | |
| cur_example = InputExample(guid=cur_id, tokens_a=tokens_a, tokens_b=tokens_b, is_next=is_next_label) | |
| # transform sample to features | |
| cur_features = convert_example_to_features(cur_example, self.seq_len, self.tokenizer) | |
| cur_tensors = (torch.tensor(cur_features.input_ids), | |
| torch.tensor(cur_features.input_mask), | |
| torch.tensor(cur_features.segment_ids), | |
| torch.tensor(cur_features.lm_label_ids), | |
| torch.tensor(cur_features.is_next)) | |
| return cur_tensors | |
| def random_sent(self, index): | |
| """ | |
| Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences | |
| from one doc. With 50% the second sentence will be a random one from another doc. | |
| :param index: int, index of sample. | |
| :return: (str, str, int), sentence 1, sentence 2, isNextSentence Label | |
| """ | |
| t1, t2 = self.get_corpus_line(index) | |
| if random.random() > 0.5: | |
| label = 0 | |
| else: | |
| t2 = self.get_random_line() | |
| label = 1 | |
| assert len(t1) > 0 | |
| assert len(t2) > 0 | |
| return t1, t2, label | |
| def get_corpus_line(self, item): | |
| """ | |
| Get one sample from corpus consisting of a pair of two subsequent lines from the same doc. | |
| :param item: int, index of sample. | |
| :return: (str, str), two subsequent sentences from corpus | |
| """ | |
| t1 = "" | |
| t2 = "" | |
| assert item < self.corpus_lines | |
| if self.on_memory: | |
| sample = self.sample_to_doc[item] | |
| t1 = self.all_docs[sample["doc_id"]][sample["line"]] | |
| t2 = self.all_docs[sample["doc_id"]][sample["line"]+1] | |
| # used later to avoid random nextSentence from same doc | |
| self.current_doc = sample["doc_id"] | |
| return t1, t2 | |
| else: | |
| if self.line_buffer is None: | |
| # read first non-empty line of file | |
| while t1 == "" : | |
| t1 = next(self.file).strip() | |
| t2 = next(self.file).strip() | |
| else: | |
| # use t2 from previous iteration as new t1 | |
| t1 = self.line_buffer | |
| t2 = next(self.file).strip() | |
| # skip empty rows that are used for separating documents and keep track of current doc id | |
| while t2 == "" or t1 == "": | |
| t1 = next(self.file).strip() | |
| t2 = next(self.file).strip() | |
| self.current_doc = self.current_doc+1 | |
| self.line_buffer = t2 | |
| assert t1 != "" | |
| assert t2 != "" | |
| return t1, t2 | |
| def get_random_line(self): | |
| """ | |
| Get random line from another document for nextSentence task. | |
| :return: str, content of one line | |
| """ | |
| # Similar to original tf repo: This outer loop should rarely go for more than one iteration for large | |
| # corpora. However, just to be careful, we try to make sure that | |
| # the random document is not the same as the document we're processing. | |
| for _ in range(10): | |
| if self.on_memory: | |
| rand_doc_idx = random.randint(0, len(self.all_docs)-1) | |
| rand_doc = self.all_docs[rand_doc_idx] | |
| line = rand_doc[random.randrange(len(rand_doc))] | |
| else: | |
| rand_index = random.randint(1, self.corpus_lines if self.corpus_lines < 1000 else 1000) | |
| #pick random line | |
| for _ in range(rand_index): | |
| line = self.get_next_line() | |
| #check if our picked random line is really from another doc like we want it to be | |
| if self.current_random_doc != self.current_doc: | |
| break | |
| return line | |
| def get_next_line(self): | |
| """ Gets next line of random_file and starts over when reaching end of file""" | |
| try: | |
| line = next(self.random_file).strip() | |
| #keep track of which document we are currently looking at to later avoid having the same doc as t1 | |
| if line == "": | |
| self.current_random_doc = self.current_random_doc + 1 | |
| line = next(self.random_file).strip() | |
| except StopIteration: | |
| self.random_file.close() | |
| self.random_file = open(self.corpus_path, "r", encoding=self.encoding) | |
| line = next(self.random_file).strip() | |
| return line | |
| class InputExample(object): | |
| """A single training/test example for the language model.""" | |
| def __init__(self, guid, tokens_a, tokens_b=None, is_next=None, lm_labels=None): | |
| """Constructs a InputExample. | |
| Args: | |
| guid: Unique id for the example. | |
| tokens_a: string. The untokenized text of the first sequence. For single | |
| sequence tasks, only this sequence must be specified. | |
| tokens_b: (Optional) string. The untokenized text of the second sequence. | |
| Only must be specified for sequence pair 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.tokens_a = tokens_a | |
| self.tokens_b = tokens_b | |
| self.is_next = is_next # nextSentence | |
| self.lm_labels = lm_labels # masked words for language model | |
| class InputFeatures(object): | |
| """A single set of features of data.""" | |
| def __init__(self, input_ids, input_mask, segment_ids, is_next, lm_label_ids): | |
| self.input_ids = input_ids | |
| self.input_mask = input_mask | |
| self.segment_ids = segment_ids | |
| self.is_next = is_next | |
| self.lm_label_ids = lm_label_ids | |
| def random_word(tokens, tokenizer): | |
| """ | |
| Masking some random tokens for Language Model task with probabilities as in the original BERT paper. | |
| :param tokens: list of str, tokenized sentence. | |
| :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here) | |
| :return: (list of str, list of int), masked tokens and related labels for LM prediction | |
| """ | |
| output_label = [] | |
| for i, token in enumerate(tokens): | |
| prob = random.random() | |
| # mask token with 15% probability | |
| if prob < 0.15: | |
| prob /= 0.15 | |
| # 80% randomly change token to mask token | |
| if prob < 0.8: | |
| tokens[i] = "[MASK]" | |
| # 10% randomly change token to random token | |
| elif prob < 0.9: | |
| tokens[i] = random.choice(list(tokenizer.vocab.items()))[0] | |
| # -> rest 10% randomly keep current token | |
| # append current token to output (we will predict these later) | |
| try: | |
| output_label.append(tokenizer.vocab[token]) | |
| except KeyError: | |
| # For unknown words (should not occur with BPE vocab) | |
| output_label.append(tokenizer.vocab["[UNK]"]) | |
| logger.warning("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token)) | |
| else: | |
| # no masking token (will be ignored by loss function later) | |
| output_label.append(-1) | |
| return tokens, output_label | |
| def convert_example_to_features(example, max_seq_length, tokenizer): | |
| """ | |
| Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with | |
| IDs, LM labels, input_mask, CLS and SEP tokens etc. | |
| :param example: InputExample, containing sentence input as strings and is_next label | |
| :param max_seq_length: int, maximum length of sequence. | |
| :param tokenizer: Tokenizer | |
| :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training) | |
| """ | |
| tokens_a = example.tokens_a | |
| tokens_b = example.tokens_b | |
| # Modifies `tokens_a` and `tokens_b` in place so that the total | |
| # length is less than the specified length. | |
| # Account for [CLS], [SEP], [SEP] with "- 3" | |
| _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) | |
| tokens_a, t1_label = random_word(tokens_a, tokenizer) | |
| tokens_b, t2_label = random_word(tokens_b, tokenizer) | |
| # concatenate lm labels and account for CLS, SEP, SEP | |
| lm_label_ids = ([-1] + t1_label + [-1] + t2_label + [-1]) | |
| # 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 | |
| # | |
| # Where "type_ids" are used to indicate whether this is the first | |
| # sequence or the second 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 unambigiously 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 = [] | |
| segment_ids = [] | |
| tokens.append("[CLS]") | |
| segment_ids.append(0) | |
| for token in tokens_a: | |
| tokens.append(token) | |
| segment_ids.append(0) | |
| tokens.append("[SEP]") | |
| segment_ids.append(0) | |
| assert len(tokens_b) > 0 | |
| for token in tokens_b: | |
| tokens.append(token) | |
| segment_ids.append(1) | |
| tokens.append("[SEP]") | |
| segment_ids.append(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. | |
| while len(input_ids) < max_seq_length: | |
| input_ids.append(0) | |
| input_mask.append(0) | |
| segment_ids.append(0) | |
| lm_label_ids.append(-1) | |
| assert len(input_ids) == max_seq_length | |
| assert len(input_mask) == max_seq_length | |
| assert len(segment_ids) == max_seq_length | |
| assert len(lm_label_ids) == max_seq_length | |
| if example.guid < 5: | |
| 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("LM label: %s " % (lm_label_ids)) | |
| logger.info("Is next sentence label: %s " % (example.is_next)) | |
| features = InputFeatures(input_ids=input_ids, | |
| input_mask=input_mask, | |
| segment_ids=segment_ids, | |
| lm_label_ids=lm_label_ids, | |
| is_next=example.is_next) | |
| return features | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| ## Required parameters | |
| parser.add_argument("--train_corpus", | |
| default=None, | |
| type=str, | |
| required=True, | |
| help="The input train corpus.") | |
| 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-base-multilingual, bert-base-chinese.") | |
| 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("--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("--train_batch_size", | |
| default=32, | |
| type=int, | |
| help="Total batch size for training.") | |
| parser.add_argument("--learning_rate", | |
| default=3e-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("--on_memory", | |
| action='store_true', | |
| help="Whether to load train samples into memory or use disk") | |
| parser.add_argument("--do_lower_case", | |
| action='store_true', | |
| help="Whether to lower case the input text. True for uncased models, False for cased models.") | |
| 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('--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") | |
| args = parser.parse_args() | |
| if args.local_rank == -1 or args.no_cuda: | |
| device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") | |
| n_gpu = torch.cuda.device_count() | |
| else: | |
| torch.cuda.set_device(args.local_rank) | |
| device = torch.device("cuda", args.local_rank) | |
| n_gpu = 1 | |
| # Initializes the distributed backend which will take care of sychronizing nodes/GPUs | |
| torch.distributed.init_process_group(backend='nccl') | |
| logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( | |
| device, n_gpu, bool(args.local_rank != -1), args.fp16)) | |
| if args.gradient_accumulation_steps < 1: | |
| raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( | |
| args.gradient_accumulation_steps)) | |
| args.train_batch_size = 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: | |
| raise ValueError("Training is currently the only implemented execution option. Please set `do_train`.") | |
| 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)) | |
| if not os.path.exists(args.output_dir): | |
| os.makedirs(args.output_dir) | |
| tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) | |
| #train_examples = None | |
| num_train_optimization_steps = None | |
| if args.do_train: | |
| print("Loading Train Dataset", args.train_corpus) | |
| train_dataset = BERTDataset(args.train_corpus, tokenizer, seq_len=args.max_seq_length, | |
| corpus_lines=None, on_memory=args.on_memory) | |
| num_train_optimization_steps = int( | |
| len(train_dataset) / 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 | |
| model = BertForPreTraining.from_pretrained(args.bert_model) | |
| if args.fp16: | |
| model.half() | |
| model.to(device) | |
| if args.local_rank != -1: | |
| try: | |
| from apex.parallel import DistributedDataParallel as DDP | |
| except ImportError: | |
| raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") | |
| model = DDP(model) | |
| elif n_gpu > 1: | |
| model = torch.nn.DataParallel(model) | |
| # Prepare optimizer | |
| if args.do_train: | |
| 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 | |
| if args.do_train: | |
| logger.info("***** Running training *****") | |
| logger.info(" Num examples = %d", len(train_dataset)) | |
| logger.info(" Batch size = %d", args.train_batch_size) | |
| logger.info(" Num steps = %d", num_train_optimization_steps) | |
| if args.local_rank == -1: | |
| train_sampler = RandomSampler(train_dataset) | |
| else: | |
| #TODO: check if this works with current data generator from disk that relies on next(file) | |
| # (it doesn't return item back by index) | |
| train_sampler = DistributedSampler(train_dataset) | |
| train_dataloader = DataLoader(train_dataset, 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")): | |
| batch = tuple(t.to(device) for t in batch) | |
| input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch | |
| loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next) | |
| 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, args.warmup_proportion) | |
| for param_group in optimizer.param_groups: | |
| param_group['lr'] = lr_this_step | |
| optimizer.step() | |
| optimizer.zero_grad() | |
| global_step += 1 | |
| # Save a trained model | |
| logger.info("** ** * Saving fine - tuned model ** ** * ") | |
| model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self | |
| output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") | |
| if args.do_train: | |
| torch.save(model_to_save.state_dict(), output_model_file) | |
| 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 accuracy(out, labels): | |
| outputs = np.argmax(out, axis=1) | |
| return np.sum(outputs == labels) | |
| if __name__ == "__main__": | |
| main() |
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