mirror of
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updating run_xlnet_classifier
This commit is contained in:
thomwolf
5
.gitignore
vendored
5
.gitignore
vendored
@ -123,4 +123,7 @@ tensorflow_code
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# Models
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models
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proc_data
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proc_data
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# examples
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examples/runs
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@ -54,91 +54,58 @@ 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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parser.add_argument("--data_dir", default=None, type=str, 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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parser.add_argument("--task_name", default=None, type=str, 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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parser.add_argument("--output_dir", default=None, type=str, 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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# training
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parser.add_argument("--do_train", 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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parser.add_argument("--learning_rate", default=5e-5, 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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parser.add_argument("--num_train_epochs", default=3.0, 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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parser.add_argument("--warmup_proportion", default=0.1, 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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parser.add_argument("--train_batch_size", default=32, type=int,
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help="Total batch size for training.")
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parser.add_argument('--gradient_accumulation_steps', type=int, 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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parser.add_argument('--fp16', 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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parser.add_argument('--loss_scale', 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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# evaluation
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parser.add_argument("--do_eval", action='store_true',
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help="Whether to run eval on the dev set.")
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parser.add_argument("--eval_batch_size", default=8, type=int,
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help="Total batch size for eval.")
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# Model
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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("--do_lower_case", 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("--cache_dir", default="", type=str,
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help="Where do you want to store the pre-trained models downloaded from s3")
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# task specific
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parser.add_argument("--max_seq_length", default=128, 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('--overwrite_output_dir', action='store_true',
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help="Overwrite the content of the output directory")
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# Misc
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parser.add_argument("--no_cuda", action='store_true',
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help="Whether not to use CUDA when available")
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parser.add_argument("--local_rank", type=int, default=-1,
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help="local_rank for distributed training on gpus")
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parser.add_argument('--seed', type=int, default=42,
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help="random seed for initialization")
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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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@ -306,7 +273,7 @@ def main():
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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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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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@ -420,7 +387,7 @@ def main():
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label_ids = label_ids.to(device)
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with torch.no_grad():
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logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
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logits, _ = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
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# create eval loss and other metric required by the task
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if output_mode == "classification":
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@ -501,7 +468,7 @@ def main():
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label_ids = label_ids.to(device)
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with torch.no_grad():
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logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None)
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logits, _ = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None)
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loss_fct = CrossEntropyLoss()
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tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
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398
examples/run_xlnet_squad.py
Normal file
398
examples/run_xlnet_squad.py
Normal file
@ -0,0 +1,398 @@
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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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"""Run BERT on SQuAD."""
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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 random
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import sys
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from io import open
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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 tqdm import tqdm, trange
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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 BertForQuestionAnswering
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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_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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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("--bert_model", default=None, type=str, required=True,
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help="Bert pre-trained model selected in the list: bert-base-uncased, "
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"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
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"bert-base-multilingual-cased, bert-base-chinese.")
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parser.add_argument("--output_dir", default=None, type=str, required=True,
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help="The output directory where the model checkpoints and predictions will be written.")
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## Other parameters
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parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
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parser.add_argument("--predict_file", default=None, type=str,
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help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
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parser.add_argument("--max_seq_length", default=384, type=int,
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help="The maximum total input sequence length after WordPiece tokenization. Sequences "
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"longer than this will be truncated, and sequences shorter than this will be padded.")
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parser.add_argument("--doc_stride", default=128, type=int,
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help="When splitting up a long document into chunks, how much stride to take between chunks.")
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parser.add_argument("--max_query_length", default=64, type=int,
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help="The maximum number of tokens for the question. Questions longer than this will "
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"be truncated to this length.")
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parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
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parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.")
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parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
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parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
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parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs", default=3.0, type=float,
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help="Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion", default=0.1, type=float,
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help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
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"of training.")
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parser.add_argument("--n_best_size", default=20, type=int,
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help="The total number of n-best predictions to generate in the nbest_predictions.json "
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"output file.")
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parser.add_argument("--max_answer_length", default=30, type=int,
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help="The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another.")
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parser.add_argument("--verbose_logging", action='store_true',
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help="If true, all of the warnings related to data processing will be printed. "
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"A number of warnings are expected for a normal SQuAD evaluation.")
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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('--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("--do_lower_case",
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action='store_true',
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help="Whether to lower case the input text. True for uncased models, False for cased models.")
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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('--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('--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('--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('--version_2_with_negative',
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action='store_true',
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help='If true, the SQuAD examples contain some that do not have an answer.')
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parser.add_argument('--null_score_diff_threshold',
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type=float, default=0.0,
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help="If null_score - best_non_null is greater than the threshold predict null.")
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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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print(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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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_predict:
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raise ValueError("At least one of `do_train` or `do_predict` must be True.")
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if args.do_train:
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if not args.train_file:
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raise ValueError(
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"If `do_train` is True, then `train_file` must be specified.")
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if args.do_predict:
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if not args.predict_file:
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raise ValueError(
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"If `do_predict` is True, then `predict_file` must be specified.")
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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.")
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if not os.path.exists(args.output_dir):
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os.makedirs(args.output_dir)
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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 = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
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model = BertForQuestionAnswering.from_pretrained(args.bert_model)
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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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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 = read_squad_examples(
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input_file=args.train_file, is_training=True, version_2_with_negative=args.version_2_with_negative)
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cached_train_features_file = args.train_file+'_{0}_{1}_{2}_{3}'.format(
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list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length))
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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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examples=train_examples,
|
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tokenizer=tokenizer,
|
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max_seq_length=args.max_seq_length,
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doc_stride=args.doc_stride,
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max_query_length=args.max_query_length,
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is_training=True)
|
||||
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
|
||||
logger.info(" Saving train features into cached file %s", cached_train_features_file)
|
||||
with open(cached_train_features_file, "wb") as writer:
|
||||
pickle.dump(train_features, writer)
|
||||
|
||||
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_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
|
||||
all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
|
||||
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
|
||||
all_start_positions, all_end_positions)
|
||||
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)
|
||||
num_train_optimization_steps = len(train_dataloader) // 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 optimizer
|
||||
param_optimizer = list(model.named_parameters())
|
||||
|
||||
# hack to remove pooler, which is not used
|
||||
# thus it produce None grad that break apex
|
||||
param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
|
||||
|
||||
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
|
||||
|
||||
logger.info("***** Running training *****")
|
||||
logger.info(" Num orig examples = %d", len(train_examples))
|
||||
logger.info(" Num split examples = %d", len(train_features))
|
||||
logger.info(" Batch size = %d", args.train_batch_size)
|
||||
logger.info(" Num steps = %d", num_train_optimization_steps)
|
||||
|
||||
model.train()
|
||||
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
|
||||
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
|
||||
if n_gpu == 1:
|
||||
batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self
|
||||
input_ids, input_mask, segment_ids, start_positions, end_positions = batch
|
||||
loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
|
||||
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()
|
||||
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 and 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
|
||||
if args.local_rank in [-1, 0]:
|
||||
tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
|
||||
tb_writer.add_scalar('loss', loss.item(), global_step)
|
||||
|
||||
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 = BertForQuestionAnswering.from_pretrained(args.output_dir)
|
||||
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
||||
|
||||
# Good practice: save your training arguments together with the trained model
|
||||
output_args_file = os.path.join(args.output_dir, 'training_args.bin')
|
||||
torch.save(args, output_args_file)
|
||||
else:
|
||||
model = BertForQuestionAnswering.from_pretrained(args.bert_model)
|
||||
|
||||
model.to(device)
|
||||
|
||||
if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
||||
eval_examples = read_squad_examples(
|
||||
input_file=args.predict_file, is_training=False, version_2_with_negative=args.version_2_with_negative)
|
||||
eval_features = convert_examples_to_features(
|
||||
examples=eval_examples,
|
||||
tokenizer=tokenizer,
|
||||
max_seq_length=args.max_seq_length,
|
||||
doc_stride=args.doc_stride,
|
||||
max_query_length=args.max_query_length,
|
||||
is_training=False)
|
||||
|
||||
logger.info("***** Running predictions *****")
|
||||
logger.info(" Num orig examples = %d", len(eval_examples))
|
||||
logger.info(" Num split examples = %d", len(eval_features))
|
||||
logger.info(" Batch size = %d", args.predict_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_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
|
||||
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
|
||||
# Run prediction for full data
|
||||
eval_sampler = SequentialSampler(eval_data)
|
||||
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)
|
||||
|
||||
model.eval()
|
||||
all_results = []
|
||||
logger.info("Start evaluating")
|
||||
for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]):
|
||||
if len(all_results) % 1000 == 0:
|
||||
logger.info("Processing example: %d" % (len(all_results)))
|
||||
input_ids = input_ids.to(device)
|
||||
input_mask = input_mask.to(device)
|
||||
segment_ids = segment_ids.to(device)
|
||||
with torch.no_grad():
|
||||
batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)
|
||||
for i, example_index in enumerate(example_indices):
|
||||
start_logits = batch_start_logits[i].detach().cpu().tolist()
|
||||
end_logits = batch_end_logits[i].detach().cpu().tolist()
|
||||
eval_feature = eval_features[example_index.item()]
|
||||
unique_id = int(eval_feature.unique_id)
|
||||
all_results.append(RawResult(unique_id=unique_id,
|
||||
start_logits=start_logits,
|
||||
end_logits=end_logits))
|
||||
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
|
||||
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
|
||||
output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json")
|
||||
write_predictions(eval_examples, eval_features, all_results,
|
||||
args.n_best_size, args.max_answer_length,
|
||||
args.do_lower_case, output_prediction_file,
|
||||
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
|
||||
args.version_2_with_negative, args.null_score_diff_threshold)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -606,7 +606,7 @@ class BertPreTrainedModel(nn.Module):
|
||||
))
|
||||
self.config = config
|
||||
|
||||
def init_bert_weights(self, module):
|
||||
def init_weights(self, module):
|
||||
""" Initialize the weights.
|
||||
"""
|
||||
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||||
@ -823,7 +823,7 @@ class BertModel(BertPreTrainedModel):
|
||||
self.encoder = BertEncoder(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.pooler = BertPooler(config)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def prune_heads(self, heads_to_prune):
|
||||
""" Prunes heads of the model.
|
||||
@ -951,7 +951,7 @@ class BertForPreTraining(BertPreTrainedModel):
|
||||
self.bert = BertModel(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, head_mask=None):
|
||||
outputs = self.bert(input_ids, token_type_ids, attention_mask,
|
||||
@ -1030,7 +1030,7 @@ class BertForMaskedLM(BertPreTrainedModel):
|
||||
self.bert = BertModel(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
|
||||
outputs = self.bert(input_ids, token_type_ids, attention_mask,
|
||||
@ -1105,7 +1105,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
|
||||
self.bert = BertModel(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.cls = BertOnlyNSPHead(config)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None, head_mask=None):
|
||||
outputs = self.bert(input_ids, token_type_ids, attention_mask,
|
||||
@ -1184,7 +1184,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.classifier = nn.Linear(config.hidden_size, num_labels)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
|
||||
outputs = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, head_mask=head_mask)
|
||||
@ -1261,7 +1261,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.classifier = nn.Linear(config.hidden_size, 1)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
|
||||
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
||||
@ -1343,7 +1343,7 @@ class BertForTokenClassification(BertPreTrainedModel):
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.classifier = nn.Linear(config.hidden_size, num_labels)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
|
||||
outputs = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, head_mask=head_mask)
|
||||
@ -1428,7 +1428,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
|
||||
self.bert = BertModel(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
||||
self.apply(self.init_bert_weights)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None,
|
||||
end_positions=None, head_mask=None):
|
||||
|
||||
@ -633,7 +633,7 @@ class XLNetPreTrainedModel(nn.Module):
|
||||
))
|
||||
self.config = config
|
||||
|
||||
def init_xlnet_weights(self, module):
|
||||
def init_weights(self, module):
|
||||
""" Initialize the weights.
|
||||
"""
|
||||
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||||
@ -904,14 +904,14 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
pos_emb = pos_emb.to(next(self.parameters()))
|
||||
return pos_emb
|
||||
|
||||
def forward(self, inp_k, seg_id=None, input_mask=None,
|
||||
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
|
||||
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
||||
output_all_encoded_layers=True, head_mask=None):
|
||||
"""
|
||||
Args:
|
||||
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
||||
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
|
||||
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
attention_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
|
||||
0 for real tokens and 1 for padding.
|
||||
mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
||||
from previous batches. The length of the list equals n_layer.
|
||||
@ -945,8 +945,8 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
# but we want a unified interface in the library with the batch size on the first dimension
|
||||
# so we move here the first dimension (batch) to the end
|
||||
inp_k = inp_k.transpose(0, 1).contiguous()
|
||||
seg_id = seg_id.transpose(0, 1).contiguous() if seg_id is not None else None
|
||||
input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
|
||||
token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
|
||||
attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
|
||||
perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
|
||||
target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
|
||||
inp_q = inp_q.transpose(0, 1).contiguous() if inp_q is not None else None
|
||||
@ -969,11 +969,11 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
raise ValueError('Unsupported attention type: {}'.format(self.attn_type))
|
||||
|
||||
# data mask: input mask & perm mask
|
||||
if input_mask is not None and perm_mask is not None:
|
||||
data_mask = input_mask[None] + perm_mask
|
||||
elif input_mask is not None and perm_mask is None:
|
||||
data_mask = input_mask[None]
|
||||
elif input_mask is None and perm_mask is not None:
|
||||
if attention_mask is not None and perm_mask is not None:
|
||||
data_mask = attention_mask[None] + perm_mask
|
||||
elif attention_mask is not None and perm_mask is None:
|
||||
data_mask = attention_mask[None]
|
||||
elif attention_mask is None and perm_mask is not None:
|
||||
data_mask = perm_mask
|
||||
else:
|
||||
data_mask = None
|
||||
@ -1011,13 +1011,13 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
output_g = None
|
||||
|
||||
##### Segment embedding
|
||||
if seg_id is not None:
|
||||
# Convert `seg_id` to one-hot `seg_mat`
|
||||
if token_type_ids is not None:
|
||||
# Convert `token_type_ids` to one-hot `seg_mat`
|
||||
mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
|
||||
cat_ids = torch.cat([mem_pad, seg_id], dim=0)
|
||||
cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
|
||||
|
||||
# `1` indicates not in the same segment [qlen x klen x bsz]
|
||||
seg_mat = (seg_id[:, None] != cat_ids[None, :]).long()
|
||||
seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()
|
||||
seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float)
|
||||
else:
|
||||
seg_mat = None
|
||||
@ -1076,8 +1076,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
|
||||
Inputs:
|
||||
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
||||
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
|
||||
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
attention_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
|
||||
0 for real tokens and 1 for padding.
|
||||
mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
||||
from previous batches. The length of the list equals n_layer.
|
||||
@ -1112,14 +1112,14 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
```python
|
||||
# Already been converted into WordPiece token ids
|
||||
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
||||
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
||||
|
||||
config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
|
||||
n_layer=12, num_attention_heads=12, intermediate_size=3072)
|
||||
|
||||
model = modeling.XLNetModel(config=config)
|
||||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
||||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, attention_mask)
|
||||
```
|
||||
"""
|
||||
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
||||
@ -1134,7 +1134,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
|
||||
# Tie weights
|
||||
|
||||
self.apply(self.init_xlnet_weights)
|
||||
self.apply(self.init_weights)
|
||||
self.tie_weights()
|
||||
|
||||
def tie_weights(self):
|
||||
@ -1142,14 +1142,14 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
"""
|
||||
self.lm_loss.weight = self.transformer.word_embedding.weight
|
||||
|
||||
def forward(self, inp_k, seg_id=None, input_mask=None,
|
||||
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
|
||||
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
||||
target=None, output_all_encoded_layers=True, head_mask=None):
|
||||
"""
|
||||
Args:
|
||||
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
||||
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
input_mask: float32 Tensor in shape [bsz, len], the input mask.
|
||||
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
attention_mask: float32 Tensor in shape [bsz, len], the input mask.
|
||||
0 for real tokens and 1 for padding.
|
||||
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
||||
from previous batches. The length of the list equals n_layer.
|
||||
@ -1171,7 +1171,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
summary_type: str, "last", "first", "mean", or "attn". The method
|
||||
to pool the input to get a vector representation.
|
||||
"""
|
||||
output, hidden_states, new_mems = self.transformer(inp_k, seg_id, input_mask,
|
||||
output, hidden_states, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
|
||||
mems, perm_mask, target_mapping, inp_q,
|
||||
output_all_encoded_layers, head_mask)
|
||||
|
||||
@ -1200,7 +1200,7 @@ class XLNetSequenceSummary(nn.Module):
|
||||
super(XLNetSequenceSummary, self).__init__()
|
||||
self.summary_type = summary_type
|
||||
if use_proj:
|
||||
self.summary = nn.Linear(config.hidden_size, num_labels)
|
||||
self.summary = nn.Linear(config.d_model, config.d_model)
|
||||
else:
|
||||
self.summary = None
|
||||
if summary_type == 'attn':
|
||||
@ -1211,19 +1211,20 @@ class XLNetSequenceSummary(nn.Module):
|
||||
self.dropout = nn.Dropout(config.dropout)
|
||||
self.activation = nn.Tanh()
|
||||
|
||||
def forward(self, hidden_states, input_mask=None):
|
||||
def forward(self, hidden_states):
|
||||
""" hidden_states: float Tensor in shape [bsz, seq_len, d_model], the hidden-states of the last layer."""
|
||||
if self.summary_type == 'last':
|
||||
output = hidden_states[-1]
|
||||
output = hidden_states[:, -1]
|
||||
elif self.summary_type == 'first':
|
||||
output = hidden_states[0]
|
||||
output = hidden_states[:, 0]
|
||||
elif self.summary_type == 'mean':
|
||||
output = hidden_states.mean(dim=0)
|
||||
output = hidden_states.mean(dim=1)
|
||||
elif summary_type == 'attn':
|
||||
raise NotImplementedError
|
||||
|
||||
output = self.summary(output)
|
||||
output = self.dropout(output)
|
||||
output = self.activation(output)
|
||||
output = self.dropout(output)
|
||||
return output
|
||||
|
||||
|
||||
@ -1240,8 +1241,8 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
|
||||
Inputs:
|
||||
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
||||
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
input_mask: float32 Tensor in shape [bsz, len], the input mask.
|
||||
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
attention_mask: float32 Tensor in shape [bsz, len], the input mask.
|
||||
0 for real tokens and 1 for padding.
|
||||
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
||||
from previous batches. The length of the list equals n_layer.
|
||||
@ -1277,14 +1278,14 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
```python
|
||||
# Already been converted into WordPiece token ids
|
||||
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
||||
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
||||
|
||||
config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
|
||||
n_layer=12, num_attention_heads=12, intermediate_size=3072)
|
||||
|
||||
model = modeling.XLNetModel(config=config)
|
||||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
||||
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, attention_mask)
|
||||
```
|
||||
"""
|
||||
def __init__(self, config, summary_type="last", use_proj=True, num_labels=2,
|
||||
@ -1302,17 +1303,17 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
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)
|
||||
self.loss_proj = nn.Linear(config.d_model, num_labels if not is_regression else 1)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, inp_k, seg_id=None, input_mask=None,
|
||||
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
|
||||
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
||||
target=None, output_all_encoded_layers=True, head_mask=None):
|
||||
"""
|
||||
Args:
|
||||
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
||||
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
input_mask: float32 Tensor in shape [bsz, len], the input mask.
|
||||
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
|
||||
attention_mask: float32 Tensor in shape [bsz, len], the input mask.
|
||||
0 for real tokens and 1 for padding.
|
||||
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
||||
from previous batches. The length of the list equals n_layer.
|
||||
@ -1331,7 +1332,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
Only used during pretraining for two-stream attention.
|
||||
Set to None during finetuning.
|
||||
"""
|
||||
output, _, new_mems = self.transformer(inp_k, seg_id, input_mask,
|
||||
output, _, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
|
||||
mems, perm_mask, target_mapping, inp_q,
|
||||
output_all_encoded_layers, head_mask)
|
||||
|
||||
@ -1356,3 +1357,96 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
# if self.output_attentions:
|
||||
return logits, new_mems
|
||||
# return all_attentions, encoded_layers, pooled_output
|
||||
|
||||
class XLNetForQuestionAnswering(XLNetPreTrainedModel):
|
||||
"""XLNet model for Question Answering (span extraction).
|
||||
This module is composed of the XLNet model with a linear layer on top of
|
||||
the sequence output that computes start_logits and end_logits
|
||||
|
||||
Params:
|
||||
`config`: a XLNetConfig class instance with the configuration to build a new model
|
||||
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
||||
`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
|
||||
This can be used to compute head importance metrics. Default: False
|
||||
|
||||
Inputs:
|
||||
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
|
||||
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
||||
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
||||
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
|
||||
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
|
||||
a `sentence B` token (see XLNet paper for more details).
|
||||
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
|
||||
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
||||
input sequence length in the current batch. It's the mask that we typically use for attention when
|
||||
a batch has varying length sentences.
|
||||
`start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
|
||||
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
|
||||
into account for computing the loss.
|
||||
`end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
|
||||
Positions are clamped to the length of the sequence and position outside of the sequence are not taken
|
||||
into account for computing the loss.
|
||||
`head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
||||
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
||||
|
||||
Outputs:
|
||||
if `start_positions` and `end_positions` are not `None`:
|
||||
Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
|
||||
if `start_positions` or `end_positions` is `None`:
|
||||
Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
|
||||
position tokens of shape [batch_size, sequence_length].
|
||||
|
||||
Example usage:
|
||||
```python
|
||||
# Already been converted into WordPiece token ids
|
||||
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
||||
attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
||||
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
||||
|
||||
config = XLNetConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
||||
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
||||
|
||||
model = XLNetForQuestionAnswering(config)
|
||||
start_logits, end_logits = model(input_ids, token_type_ids, attention_mask)
|
||||
```
|
||||
"""
|
||||
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
||||
super(XLNetForQuestionAnswering, self).__init__(config)
|
||||
self.output_attentions = output_attentions
|
||||
self.transformer = XLNetModel(config, output_attentions=output_attentions,
|
||||
keep_multihead_output=keep_multihead_output)
|
||||
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def forward(self, inp_k, token_type_ids=None, attention_mask=None,
|
||||
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
||||
start_positions=None, end_positions=None,
|
||||
output_all_encoded_layers=True, head_mask=None):
|
||||
output, _, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
|
||||
mems, perm_mask, target_mapping, inp_q,
|
||||
output_all_encoded_layers, head_mask)
|
||||
|
||||
logits = self.qa_outputs(output)
|
||||
start_logits, end_logits = logits.split(1, dim=-1)
|
||||
start_logits = start_logits.squeeze(-1)
|
||||
end_logits = end_logits.squeeze(-1)
|
||||
|
||||
if start_positions is not None and end_positions is not None:
|
||||
# If we are on multi-GPU, split add a dimension
|
||||
if len(start_positions.size()) > 1:
|
||||
start_positions = start_positions.squeeze(-1)
|
||||
if len(end_positions.size()) > 1:
|
||||
end_positions = end_positions.squeeze(-1)
|
||||
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
||||
ignored_index = start_logits.size(1)
|
||||
start_positions.clamp_(0, ignored_index)
|
||||
end_positions.clamp_(0, ignored_index)
|
||||
|
||||
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
||||
start_loss = loss_fct(start_logits, start_positions)
|
||||
end_loss = loss_fct(end_logits, end_positions)
|
||||
total_loss = (start_loss + end_loss) / 2
|
||||
return total_loss
|
||||
elif self.output_attentions:
|
||||
return all_attentions, start_logits, end_logits
|
||||
return start_logits, end_logits
|
||||
|
||||
Reference in New Issue
Block a user