add xlnetforsequence classif and run_classifier example for xlnet
This commit is contained in:
thomwolf
parent
c946bb51a6
commit
f6081f2255
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@ -14,7 +14,7 @@ from torch.nn import CrossEntropyLoss, MSELoss
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from pytorch_pretrained_bert import BertForSequenceClassification, BertTokenizer
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from run_classifier_dataset_utils import processors, output_modes, convert_examples_to_features, compute_metrics
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from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics
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logger = logging.getLogger(__name__)
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@ -39,7 +39,7 @@ from pytorch_pretrained_bert.modeling import BertForSequenceClassification
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from pytorch_pretrained_bert.tokenization import BertTokenizer
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from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
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from run_classifier_dataset_utils import processors, output_modes, convert_examples_to_features, compute_metrics
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from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics
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if sys.version_info[0] == 2:
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import cPickle as pickle
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@ -38,7 +38,7 @@ from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
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from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
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from pytorch_pretrained_bert.tokenization import BertTokenizer
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from run_squad_dataset_utils import read_squad_examples, convert_examples_to_features, RawResult, write_predictions
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from utils_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions
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if sys.version_info[0] == 2:
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import cPickle as pickle
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@ -0,0 +1,539 @@
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""BERT finetuning runner."""
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from __future__ import absolute_import, division, print_function
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import argparse
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import logging
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import os
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import sys
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import random
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from tqdm import tqdm, trange
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import numpy as np
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import torch
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from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
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TensorDataset)
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from torch.utils.data.distributed import DistributedSampler
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from torch.nn import CrossEntropyLoss, MSELoss
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from tensorboardX import SummaryWriter
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from pytorch_pretrained_bert.file_utils import WEIGHTS_NAME, CONFIG_NAME
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from pytorch_pretrained_bert.modeling_xlnet import XLNetForSequenceClassification
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from pytorch_pretrained_bert.tokenization_xlnet import XLNetTokenizer
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from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
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from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics
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if sys.version_info[0] == 2:
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import cPickle as pickle
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else:
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import pickle
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logger = logging.getLogger(__name__)
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def main():
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--data_dir",
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default=None,
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type=str,
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required=True,
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help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
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parser.add_argument("--xlnet_model", default="xlnet-large-cased", type=str,
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help="XLNet pre-trained model: currently only xlnet-large-cased.")
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parser.add_argument("--task_name",
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default=None,
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type=str,
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required=True,
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help="The name of the task to train.")
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parser.add_argument("--output_dir",
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default=None,
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type=str,
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required=True,
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help="The output directory where the model predictions and checkpoints will be written.")
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## Other parameters
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parser.add_argument("--cache_dir",
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default="",
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type=str,
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help="Where do you want to store the pre-trained models downloaded from s3")
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parser.add_argument("--max_seq_length",
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default=128,
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type=int,
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help="The maximum total input sequence length after WordPiece tokenization. \n"
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"Sequences longer than this will be truncated, and sequences shorter \n"
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"than this will be padded.")
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parser.add_argument("--do_train",
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action='store_true',
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help="Whether to run training.")
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parser.add_argument("--do_eval",
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action='store_true',
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help="Whether to run eval on the dev set.")
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parser.add_argument("--do_lower_case",
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action='store_true',
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help="Set this flag if you are using an uncased model.")
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parser.add_argument("--train_batch_size",
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default=32,
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type=int,
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help="Total batch size for training.")
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parser.add_argument("--eval_batch_size",
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default=8,
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type=int,
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help="Total batch size for eval.")
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parser.add_argument("--learning_rate",
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default=5e-5,
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type=float,
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help="The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs",
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default=3.0,
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type=float,
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help="Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion",
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default=0.1,
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type=float,
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help="Proportion of training to perform linear learning rate warmup for. "
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"E.g., 0.1 = 10%% of training.")
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parser.add_argument("--no_cuda",
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action='store_true',
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help="Whether not to use CUDA when available")
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parser.add_argument('--overwrite_output_dir',
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action='store_true',
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help="Overwrite the content of the output directory")
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parser.add_argument("--local_rank",
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type=int,
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default=-1,
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help="local_rank for distributed training on gpus")
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parser.add_argument('--seed',
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type=int,
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default=42,
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help="random seed for initialization")
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parser.add_argument('--gradient_accumulation_steps',
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.")
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parser.add_argument('--fp16',
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action='store_true',
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help="Whether to use 16-bit float precision instead of 32-bit")
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parser.add_argument('--loss_scale',
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type=float, default=0,
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help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
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"0 (default value): dynamic loss scaling.\n"
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"Positive power of 2: static loss scaling value.\n")
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parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
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parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
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args = parser.parse_args()
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if args.server_ip and args.server_port:
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# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
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import ptvsd
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print("Waiting for debugger attach")
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ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
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ptvsd.wait_for_attach()
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if args.local_rank == -1 or args.no_cuda:
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device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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n_gpu = torch.cuda.device_count()
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else:
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torch.cuda.set_device(args.local_rank)
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device = torch.device("cuda", args.local_rank)
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n_gpu = 1
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# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
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torch.distributed.init_process_group(backend='nccl')
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args.device = device
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logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
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datefmt = '%m/%d/%Y %H:%M:%S',
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level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
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logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
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device, n_gpu, bool(args.local_rank != -1), args.fp16))
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if args.gradient_accumulation_steps < 1:
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raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
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args.gradient_accumulation_steps))
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args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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if not args.do_train and not args.do_eval:
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raise ValueError("At least one of `do_train` or `do_eval` must be True.")
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if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
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raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
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if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(args.output_dir)
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task_name = args.task_name.lower()
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if task_name not in processors:
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raise ValueError("Task not found: %s" % (task_name))
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processor = processors[task_name]()
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output_mode = output_modes[task_name]
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label_list = processor.get_labels()
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num_labels = len(label_list)
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if args.local_rank not in [-1, 0]:
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torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
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tokenizer = XLNetTokenizer.from_pretrained(args.xlnet_model, do_lower_case=args.do_lower_case)
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model = XLNetForSequenceClassification.from_pretrained(args.xlnet_model, num_labels=num_labels)
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if args.local_rank == 0:
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torch.distributed.barrier()
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if args.fp16:
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model.half()
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model.to(device)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(model,
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device_ids=[args.local_rank],
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output_device=args.local_rank,
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find_unused_parameters=True)
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elif n_gpu > 1:
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model = torch.nn.DataParallel(model)
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global_step = 0
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nb_tr_steps = 0
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tr_loss = 0
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if args.do_train:
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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# Prepare data loader
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train_examples = processor.get_train_examples(args.data_dir)
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cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
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list(filter(None, args.xlnet_model.split('/'))).pop(),
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str(args.max_seq_length),
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str(task_name)))
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try:
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with open(cached_train_features_file, "rb") as reader:
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train_features = pickle.load(reader)
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except:
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train_features = convert_examples_to_features(
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train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
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if args.local_rank == -1 or torch.distributed.get_rank() == 0:
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logger.info(" Saving train features into cached file %s", cached_train_features_file)
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with open(cached_train_features_file, "wb") as writer:
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pickle.dump(train_features, writer)
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all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
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all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
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all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
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if output_mode == "classification":
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all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
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elif output_mode == "regression":
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all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
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train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
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if args.local_rank == -1:
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train_sampler = RandomSampler(train_data)
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else:
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train_sampler = DistributedSampler(train_data)
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train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
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num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer
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param_optimizer = list(model.named_parameters())
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no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
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{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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if args.fp16:
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try:
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from apex.optimizers import FP16_Optimizer
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from apex.optimizers import FusedAdam
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
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optimizer = FusedAdam(optimizer_grouped_parameters,
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lr=args.learning_rate,
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bias_correction=False,
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max_grad_norm=1.0)
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if args.loss_scale == 0:
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optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
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else:
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optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
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warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
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t_total=num_train_optimization_steps)
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else:
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optimizer = BertAdam(optimizer_grouped_parameters,
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lr=args.learning_rate,
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warmup=args.warmup_proportion,
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t_total=num_train_optimization_steps)
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_examples))
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logger.info(" Batch size = %d", args.train_batch_size)
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logger.info(" Num steps = %d", num_train_optimization_steps)
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model.train()
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for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
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tr_loss = 0
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nb_tr_examples, nb_tr_steps = 0, 0
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for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
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batch = tuple(t.to(device) for t in batch)
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input_ids, input_mask, segment_ids, label_ids = batch
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# define a new function to compute loss values for both output_modes
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logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
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if output_mode == "classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
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elif output_mode == "regression":
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loss_fct = MSELoss()
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loss = loss_fct(logits.view(-1), label_ids.view(-1))
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu.
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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optimizer.backward(loss)
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else:
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loss.backward()
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tr_loss += loss.item()
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nb_tr_examples += input_ids.size(0)
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nb_tr_steps += 1
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if (step + 1) % args.gradient_accumulation_steps == 0:
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if args.fp16:
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# modify learning rate with special warm up BERT uses
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# if args.fp16 is False, BertAdam is used that handles this automatically
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lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr_this_step
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optimizer.step()
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optimizer.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0]:
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tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
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tb_writer.add_scalar('loss', loss.item(), global_step)
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### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
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### Example:
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if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
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# Save a trained model, configuration and tokenizer
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model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
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# If we save using the predefined names, we can load using `from_pretrained`
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output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
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output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
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torch.save(model_to_save.state_dict(), output_model_file)
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model_to_save.config.to_json_file(output_config_file)
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tokenizer.save_vocabulary(args.output_dir)
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# Load a trained model and vocabulary that you have fine-tuned
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model = XLNetForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
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tokenizer = XLNetTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
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# Good practice: save your training arguments together with the trained model
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output_args_file = os.path.join(args.output_dir, 'training_args.bin')
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torch.save(args, output_args_file)
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else:
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model = XLNetForSequenceClassification.from_pretrained(args.xlnet_model, num_labels=num_labels)
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model.to(device)
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### Evaluation
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if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
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eval_examples = processor.get_dev_examples(args.data_dir)
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cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
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list(filter(None, args.xlnet_model.split('/'))).pop(),
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str(args.max_seq_length),
|
||||
str(task_name)))
|
||||
try:
|
||||
with open(cached_eval_features_file, "rb") as reader:
|
||||
eval_features = pickle.load(reader)
|
||||
except:
|
||||
eval_features = convert_examples_to_features(
|
||||
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
|
||||
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
|
||||
logger.info(" Saving eval features into cached file %s", cached_eval_features_file)
|
||||
with open(cached_eval_features_file, "wb") as writer:
|
||||
pickle.dump(eval_features, writer)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
if output_mode == "classification":
|
||||
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
|
||||
elif output_mode == "regression":
|
||||
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
|
||||
|
||||
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||||
# Run prediction for full data
|
||||
if args.local_rank == -1:
|
||||
eval_sampler = SequentialSampler(eval_data)
|
||||
else:
|
||||
eval_sampler = DistributedSampler(eval_data) # Note that this sampler samples randomly
|
||||
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||||
|
||||
model.eval()
|
||||
eval_loss = 0
|
||||
nb_eval_steps = 0
|
||||
preds = []
|
||||
out_label_ids = None
|
||||
|
||||
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, token_type_ids=segment_ids, attention_mask=input_mask)
|
||||
|
||||
# create eval loss and other metric required by the task
|
||||
if output_mode == "classification":
|
||||
loss_fct = CrossEntropyLoss()
|
||||
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
|
||||
elif output_mode == "regression":
|
||||
loss_fct = MSELoss()
|
||||
tmp_eval_loss = loss_fct(logits.view(-1), 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())
|
||||
out_label_ids = label_ids.detach().cpu().numpy()
|
||||
else:
|
||||
preds[0] = np.append(
|
||||
preds[0], logits.detach().cpu().numpy(), axis=0)
|
||||
out_label_ids = np.append(
|
||||
out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
|
||||
|
||||
eval_loss = eval_loss / nb_eval_steps
|
||||
preds = preds[0]
|
||||
if output_mode == "classification":
|
||||
preds = np.argmax(preds, axis=1)
|
||||
elif output_mode == "regression":
|
||||
preds = np.squeeze(preds)
|
||||
result = compute_metrics(task_name, preds, out_label_ids)
|
||||
|
||||
loss = tr_loss/global_step 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])))
|
||||
|
||||
# hack for MNLI-MM
|
||||
if task_name == "mnli":
|
||||
task_name = "mnli-mm"
|
||||
processor = processors[task_name]()
|
||||
|
||||
if os.path.exists(args.output_dir + '-MM') and os.listdir(args.output_dir + '-MM') 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 + '-MM'):
|
||||
os.makedirs(args.output_dir + '-MM')
|
||||
|
||||
eval_examples = processor.get_dev_examples(args.data_dir)
|
||||
eval_features = convert_examples_to_features(
|
||||
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
|
||||
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)
|
||||
|
||||
model.eval()
|
||||
eval_loss = 0
|
||||
nb_eval_steps = 0
|
||||
preds = []
|
||||
out_label_ids = None
|
||||
|
||||
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, token_type_ids=segment_ids, attention_mask=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())
|
||||
out_label_ids = label_ids.detach().cpu().numpy()
|
||||
else:
|
||||
preds[0] = np.append(
|
||||
preds[0], logits.detach().cpu().numpy(), axis=0)
|
||||
out_label_ids = np.append(
|
||||
out_label_ids, label_ids.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, out_label_ids)
|
||||
|
||||
loss = tr_loss/global_step 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 + '-MM', "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 __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
||||
|
|
@ -3,7 +3,7 @@ from pytorch_pretrained_bert.modeling_xlnet import (
|
|||
XLNetConfig,
|
||||
XLNetModel,
|
||||
XLNetLMHeadModel,
|
||||
XLNetForSequenceClassification
|
||||
# XLNetForSequenceClassification
|
||||
)
|
||||
|
||||
# A lot of models share the same param doc. Use a decorator
|
||||
|
|
@ -135,35 +135,35 @@ def xlnetLMHeadModel(*args, **kwargs):
|
|||
return model
|
||||
|
||||
|
||||
@_append_from_pretrained_docstring(xlnet_docstring)
|
||||
def xlnetForSequenceClassification(*args, **kwargs):
|
||||
"""
|
||||
xlnetModel is the basic XLNet Transformer model from
|
||||
"XLNet: Generalized Autoregressive Pretraining for Language Understanding"
|
||||
by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
|
||||
# @_append_from_pretrained_docstring(xlnet_docstring)
|
||||
# def xlnetForSequenceClassification(*args, **kwargs):
|
||||
# """
|
||||
# xlnetModel is the basic XLNet Transformer model from
|
||||
# "XLNet: Generalized Autoregressive Pretraining for Language Understanding"
|
||||
# by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
|
||||
|
||||
Example:
|
||||
# Load the tokenizer
|
||||
>>> import torch
|
||||
>>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlnet-large-cased')
|
||||
# Example:
|
||||
# # Load the tokenizer
|
||||
# >>> import torch
|
||||
# >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetTokenizer', 'xlnet-large-cased')
|
||||
|
||||
# Prepare tokenized input
|
||||
>>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
|
||||
>>> text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
|
||||
>>> tokenized_text1 = tokenizer.tokenize(text1)
|
||||
>>> tokenized_text2 = tokenizer.tokenize(text2)
|
||||
>>> indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
|
||||
>>> indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
|
||||
>>> tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
|
||||
>>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
|
||||
# # Prepare tokenized input
|
||||
# >>> text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
|
||||
# >>> text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
|
||||
# >>> tokenized_text1 = tokenizer.tokenize(text1)
|
||||
# >>> tokenized_text2 = tokenizer.tokenize(text2)
|
||||
# >>> indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
|
||||
# >>> indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
|
||||
# >>> tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
|
||||
# >>> mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
|
||||
|
||||
# Load xlnetForSequenceClassification
|
||||
>>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetForSequenceClassification', 'xlnet-large-cased')
|
||||
>>> model.eval()
|
||||
# # Load xlnetForSequenceClassification
|
||||
# >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'xlnetForSequenceClassification', 'xlnet-large-cased')
|
||||
# >>> model.eval()
|
||||
|
||||
# Predict sequence classes logits
|
||||
>>> with torch.no_grad():
|
||||
lm_logits, mems = model(tokens_tensor)
|
||||
"""
|
||||
model = XLNetForSequenceClassification.from_pretrained(*args, **kwargs)
|
||||
return model
|
||||
# # Predict sequence classes logits
|
||||
# >>> with torch.no_grad():
|
||||
# lm_logits, mems = model(tokens_tensor)
|
||||
# """
|
||||
# model = XLNetForSequenceClassification.from_pretrained(*args, **kwargs)
|
||||
# return model
|
||||
|
|
|
|||
|
|
@ -1194,6 +1194,38 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
|||
return logits, new_mems
|
||||
# return all_attentions, encoded_layers, pooled_output
|
||||
|
||||
class XLNetSequenceSummary(nn.Module):
|
||||
def __init__(self, config, summary_type="last", use_proj=True,
|
||||
output_attentions=False, keep_multihead_output=False):
|
||||
super(XLNetSequenceSummary, self).__init__()
|
||||
self.summary_type = summary_type
|
||||
if use_proj:
|
||||
self.summary = nn.Linear(config.hidden_size, num_labels)
|
||||
else:
|
||||
self.summary = None
|
||||
if summary_type == 'attn':
|
||||
# We should use a standard multi-head attention module with absolute positional embedding for that.
|
||||
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
|
||||
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
|
||||
raise NotImplementedError
|
||||
self.dropout = nn.Dropout(config.dropout)
|
||||
self.activation = nn.Tanh()
|
||||
|
||||
def forward(self, hidden_states, input_mask=None):
|
||||
if self.summary_type == 'last':
|
||||
output = hidden_states[-1]
|
||||
elif self.summary_type == 'first':
|
||||
output = hidden_states[0]
|
||||
elif self.summary_type == 'mean':
|
||||
output = hidden_states.mean(dim=0)
|
||||
elif summary_type == 'attn':
|
||||
raise NotImplementedError
|
||||
|
||||
output = self.summary(output)
|
||||
output = self.dropout(output)
|
||||
output = self.activation(output)
|
||||
return output
|
||||
|
||||
|
||||
class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
"""XLNet model ("XLNet: Generalized Autoregressive Pretraining for Language Understanding").
|
||||
|
|
@ -1255,19 +1287,23 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
|||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
||||
```
|
||||
"""
|
||||
def __init__(self, config, summary_type="last", output_attentions=False, keep_multihead_output=False):
|
||||
def __init__(self, config, summary_type="last", use_proj=True, num_labels=2,
|
||||
is_regression=False, output_attentions=False, keep_multihead_output=False):
|
||||
super(XLNetForSequenceClassification, self).__init__(config)
|
||||
self.output_attentions = output_attentions
|
||||
self.attn_type = config.attn_type
|
||||
self.same_length = config.same_length
|
||||
self.summary_type = summary_type
|
||||
self.is_regression = is_regression
|
||||
|
||||
self.transformer = XLNetModel(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
|
||||
|
||||
self.apply(self.init_xlnet_weights)
|
||||
self.tie_weights()
|
||||
self.sequence_summary = XLNetSequenceSummary(config, summary_type=summary_type,
|
||||
use_proj=use_proj, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.loss_proj = nn.Linear(config.d_model, num_classes if not is_regression else 1)
|
||||
self.apply(self.init_bert_weights)
|
||||
|
||||
def forward(self, inp_k, seg_id=None, input_mask=None,
|
||||
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
||||
|
|
@ -1295,17 +1331,20 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
|||
Only used during pretraining for two-stream attention.
|
||||
Set to None during finetuning.
|
||||
"""
|
||||
output, hidden_states, new_mems = self.transformer(inp_k, seg_id, input_mask,
|
||||
output, _, new_mems = self.transformer(inp_k, seg_id, input_mask,
|
||||
mems, perm_mask, target_mapping, inp_q,
|
||||
output_all_encoded_layers, head_mask)
|
||||
|
||||
logits = self.lm_loss(output)
|
||||
output = self.sequence_summary(output)
|
||||
logits = self.loss_proj(output)
|
||||
|
||||
if target is not None:
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
||||
loss = loss_fct(logits.view(-1, logits.size(-1)),
|
||||
target.view(-1))
|
||||
if self.is_regression:
|
||||
loss_fct = MSELoss()
|
||||
loss = loss_fct(logits.view(-1), target.view(-1))
|
||||
else:
|
||||
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
||||
loss = loss_fct(logits.view(-1, logits.size(-1)), target.view(-1))
|
||||
return loss, new_mems
|
||||
|
||||
# if self.output_attentions:
|
||||
|
|
|
|||
Loading…
Reference in New Issue