1144 lines
51 KiB
Python
1144 lines
51 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 collections
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import json
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import logging
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import math
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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 apex import amp
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from schedulers import LinearWarmUpScheduler
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from file_utils import PYTORCH_PRETRAINED_BERT_CACHE
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from modeling import BertForQuestionAnswering, BertConfig, WEIGHTS_NAME, CONFIG_NAME
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from optimization import BertAdam, warmup_linear
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from tokenization import (BasicTokenizer, BertTokenizer, whitespace_tokenize)
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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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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)
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logger = logging.getLogger(__name__)
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class SquadExample(object):
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"""
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A single training/test example for the Squad dataset.
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For examples without an answer, the start and end position are -1.
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"""
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def __init__(self,
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qas_id,
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question_text,
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doc_tokens,
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orig_answer_text=None,
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start_position=None,
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end_position=None,
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is_impossible=None):
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self.qas_id = qas_id
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self.question_text = question_text
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self.doc_tokens = doc_tokens
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self.orig_answer_text = orig_answer_text
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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s = ""
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s += "qas_id: %s" % (self.qas_id)
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s += ", question_text: %s" % (
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self.question_text)
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s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
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if self.start_position:
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s += ", start_position: %d" % (self.start_position)
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if self.end_position:
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s += ", end_position: %d" % (self.end_position)
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if self.is_impossible:
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s += ", is_impossible: %r" % (self.is_impossible)
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return s
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self,
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unique_id,
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example_index,
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doc_span_index,
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tokens,
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token_to_orig_map,
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token_is_max_context,
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input_ids,
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input_mask,
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segment_ids,
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start_position=None,
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end_position=None,
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is_impossible=None):
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self.unique_id = unique_id
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self.example_index = example_index
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self.doc_span_index = doc_span_index
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self.tokens = tokens
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self.token_to_orig_map = token_to_orig_map
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self.token_is_max_context = token_is_max_context
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def read_squad_examples(input_file, is_training, version_2_with_negative):
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"""Read a SQuAD json file into a list of SquadExample."""
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with open(input_file, "r", encoding='utf-8') as reader:
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input_data = json.load(reader)["data"]
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def is_whitespace(c):
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if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
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return True
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return False
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examples = []
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for entry in input_data:
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for paragraph in entry["paragraphs"]:
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paragraph_text = paragraph["context"]
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doc_tokens = []
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char_to_word_offset = []
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prev_is_whitespace = True
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for c in paragraph_text:
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if is_whitespace(c):
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prev_is_whitespace = True
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else:
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if prev_is_whitespace:
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doc_tokens.append(c)
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else:
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doc_tokens[-1] += c
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prev_is_whitespace = False
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char_to_word_offset.append(len(doc_tokens) - 1)
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for qa in paragraph["qas"]:
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qas_id = qa["id"]
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question_text = qa["question"]
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start_position = None
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end_position = None
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orig_answer_text = None
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is_impossible = False
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if is_training:
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if version_2_with_negative:
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is_impossible = qa["is_impossible"]
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if (len(qa["answers"]) != 1) and (not is_impossible):
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raise ValueError(
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"For training, each question should have exactly 1 answer.")
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if not is_impossible:
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answer = qa["answers"][0]
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orig_answer_text = answer["text"]
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answer_offset = answer["answer_start"]
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answer_length = len(orig_answer_text)
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start_position = char_to_word_offset[answer_offset]
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end_position = char_to_word_offset[answer_offset + answer_length - 1]
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# Only add answers where the text can be exactly recovered from the
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# document. If this CAN'T happen it's likely due to weird Unicode
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# stuff so we will just skip the example.
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#
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# Note that this means for training mode, every example is NOT
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# guaranteed to be preserved.
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actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
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cleaned_answer_text = " ".join(
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whitespace_tokenize(orig_answer_text))
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if actual_text.find(cleaned_answer_text) == -1:
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logger.warning("Could not find answer: '%s' vs. '%s'",
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actual_text, cleaned_answer_text)
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continue
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else:
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start_position = -1
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end_position = -1
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orig_answer_text = ""
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example = SquadExample(
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qas_id=qas_id,
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question_text=question_text,
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doc_tokens=doc_tokens,
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orig_answer_text=orig_answer_text,
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start_position=start_position,
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end_position=end_position,
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is_impossible=is_impossible)
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examples.append(example)
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return examples
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def convert_examples_to_features(examples, tokenizer, max_seq_length,
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doc_stride, max_query_length, is_training):
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"""Loads a data file into a list of `InputBatch`s."""
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unique_id = 1000000000
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features = []
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for (example_index, example) in enumerate(examples):
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query_tokens = tokenizer.tokenize(example.question_text)
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if len(query_tokens) > max_query_length:
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query_tokens = query_tokens[0:max_query_length]
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tok_to_orig_index = []
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orig_to_tok_index = []
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all_doc_tokens = []
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for (i, token) in enumerate(example.doc_tokens):
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orig_to_tok_index.append(len(all_doc_tokens))
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sub_tokens = tokenizer.tokenize(token)
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for sub_token in sub_tokens:
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tok_to_orig_index.append(i)
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all_doc_tokens.append(sub_token)
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tok_start_position = None
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tok_end_position = None
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if is_training and example.is_impossible:
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tok_start_position = -1
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tok_end_position = -1
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if is_training and not example.is_impossible:
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tok_start_position = orig_to_tok_index[example.start_position]
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if example.end_position < len(example.doc_tokens) - 1:
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tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
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else:
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tok_end_position = len(all_doc_tokens) - 1
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(tok_start_position, tok_end_position) = _improve_answer_span(
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all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
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example.orig_answer_text)
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# The -3 accounts for [CLS], [SEP] and [SEP]
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max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
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# We can have documents that are longer than the maximum sequence length.
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# To deal with this we do a sliding window approach, where we take chunks
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# of the up to our max length with a stride of `doc_stride`.
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_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
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"DocSpan", ["start", "length"])
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doc_spans = []
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start_offset = 0
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while start_offset < len(all_doc_tokens):
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length = len(all_doc_tokens) - start_offset
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if length > max_tokens_for_doc:
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length = max_tokens_for_doc
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doc_spans.append(_DocSpan(start=start_offset, length=length))
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if start_offset + length == len(all_doc_tokens):
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break
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start_offset += min(length, doc_stride)
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for (doc_span_index, doc_span) in enumerate(doc_spans):
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tokens = []
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token_to_orig_map = {}
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token_is_max_context = {}
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segment_ids = []
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tokens.append("[CLS]")
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segment_ids.append(0)
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for token in query_tokens:
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tokens.append(token)
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segment_ids.append(0)
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tokens.append("[SEP]")
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segment_ids.append(0)
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for i in range(doc_span.length):
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split_token_index = doc_span.start + i
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token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
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is_max_context = _check_is_max_context(doc_spans, doc_span_index,
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split_token_index)
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token_is_max_context[len(tokens)] = is_max_context
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tokens.append(all_doc_tokens[split_token_index])
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segment_ids.append(1)
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tokens.append("[SEP]")
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segment_ids.append(1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length.
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while len(input_ids) < max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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start_position = None
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end_position = None
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if is_training and not example.is_impossible:
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# For training, if our document chunk does not contain an annotation
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# we throw it out, since there is nothing to predict.
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doc_start = doc_span.start
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doc_end = doc_span.start + doc_span.length - 1
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out_of_span = False
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if not (tok_start_position >= doc_start and
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tok_end_position <= doc_end):
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out_of_span = True
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if out_of_span:
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start_position = 0
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end_position = 0
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else:
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doc_offset = len(query_tokens) + 2
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start_position = tok_start_position - doc_start + doc_offset
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end_position = tok_end_position - doc_start + doc_offset
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if is_training and example.is_impossible:
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start_position = 0
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end_position = 0
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if example_index < 20:
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logger.info("*** Example ***")
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logger.info("unique_id: %s" % (unique_id))
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logger.info("example_index: %s" % (example_index))
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logger.info("doc_span_index: %s" % (doc_span_index))
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logger.info("tokens: %s" % " ".join(tokens))
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logger.info("token_to_orig_map: %s" % " ".join([
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"%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
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logger.info("token_is_max_context: %s" % " ".join([
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"%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
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]))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info(
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"input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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if is_training and example.is_impossible:
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logger.info("impossible example")
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if is_training and not example.is_impossible:
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answer_text = " ".join(tokens[start_position:(end_position + 1)])
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logger.info("start_position: %d" % (start_position))
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logger.info("end_position: %d" % (end_position))
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logger.info(
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"answer: %s" % (answer_text))
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features.append(
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InputFeatures(
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unique_id=unique_id,
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example_index=example_index,
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doc_span_index=doc_span_index,
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tokens=tokens,
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token_to_orig_map=token_to_orig_map,
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token_is_max_context=token_is_max_context,
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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start_position=start_position,
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end_position=end_position,
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is_impossible=example.is_impossible))
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unique_id += 1
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return features
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
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orig_answer_text):
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"""Returns tokenized answer spans that better match the annotated answer."""
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# The SQuAD annotations are character based. We first project them to
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# whitespace-tokenized words. But then after WordPiece tokenization, we can
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# often find a "better match". For example:
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#
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# Question: What year was John Smith born?
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# Context: The leader was John Smith (1895-1943).
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# Answer: 1895
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#
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# The original whitespace-tokenized answer will be "(1895-1943).". However
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# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
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# the exact answer, 1895.
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#
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# However, this is not always possible. Consider the following:
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#
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# Question: What country is the top exporter of electornics?
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# Context: The Japanese electronics industry is the lagest in the world.
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# Answer: Japan
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#
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# In this case, the annotator chose "Japan" as a character sub-span of
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# the word "Japanese". Since our WordPiece tokenizer does not split
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# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
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# in SQuAD, but does happen.
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tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
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for new_start in range(input_start, input_end + 1):
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for new_end in range(input_end, new_start - 1, -1):
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text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
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if text_span == tok_answer_text:
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return (new_start, new_end)
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return (input_start, input_end)
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def _check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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# Because of the sliding window approach taken to scoring documents, a single
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# token can appear in multiple documents. E.g.
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# Doc: the man went to the store and bought a gallon of milk
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# Span A: the man went to the
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# Span B: to the store and bought
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# Span C: and bought a gallon of
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# ...
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#
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# Now the word 'bought' will have two scores from spans B and C. We only
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# want to consider the score with "maximum context", which we define as
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# the *minimum* of its left and right context (the *sum* of left and
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# right context will always be the same, of course).
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#
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# In the example the maximum context for 'bought' would be span C since
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# it has 1 left context and 3 right context, while span B has 4 left context
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# and 0 right context.
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span.start + doc_span.length - 1
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if position < doc_span.start:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span.start
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num_right_context = end - position
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score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
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if best_score is None or score > best_score:
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best_score = score
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best_span_index = span_index
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return cur_span_index == best_span_index
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RawResult = collections.namedtuple("RawResult",
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["unique_id", "start_logits", "end_logits"])
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def write_predictions(all_examples, all_features, all_results, n_best_size,
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max_answer_length, do_lower_case, output_prediction_file,
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output_nbest_file, output_null_log_odds_file, verbose_logging,
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version_2_with_negative, null_score_diff_threshold):
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"""Write final predictions to the json file and log-odds of null if needed."""
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logger.info("Writing predictions to: %s" % (output_prediction_file))
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logger.info("Writing nbest to: %s" % (output_nbest_file))
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example_index_to_features = collections.defaultdict(list)
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for feature in all_features:
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example_index_to_features[feature.example_index].append(feature)
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unique_id_to_result = {}
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for result in all_results:
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unique_id_to_result[result.unique_id] = result
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_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
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"PrelimPrediction",
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["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
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all_predictions = collections.OrderedDict()
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all_nbest_json = collections.OrderedDict()
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scores_diff_json = collections.OrderedDict()
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for (example_index, example) in enumerate(all_examples):
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features = example_index_to_features[example_index]
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prelim_predictions = []
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# keep track of the minimum score of null start+end of position 0
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|
score_null = 1000000 # large and positive
|
|
min_null_feature_index = 0 # the paragraph slice with min mull score
|
|
null_start_logit = 0 # the start logit at the slice with min null score
|
|
null_end_logit = 0 # the end logit at the slice with min null score
|
|
for (feature_index, feature) in enumerate(features):
|
|
result = unique_id_to_result[feature.unique_id]
|
|
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
|
|
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
|
|
# if we could have irrelevant answers, get the min score of irrelevant
|
|
if version_2_with_negative:
|
|
feature_null_score = result.start_logits[0] + result.end_logits[0]
|
|
if feature_null_score < score_null:
|
|
score_null = feature_null_score
|
|
min_null_feature_index = feature_index
|
|
null_start_logit = result.start_logits[0]
|
|
null_end_logit = result.end_logits[0]
|
|
for start_index in start_indexes:
|
|
for end_index in end_indexes:
|
|
# We could hypothetically create invalid predictions, e.g., predict
|
|
# that the start of the span is in the question. We throw out all
|
|
# invalid predictions.
|
|
if start_index >= len(feature.tokens):
|
|
continue
|
|
if end_index >= len(feature.tokens):
|
|
continue
|
|
if start_index not in feature.token_to_orig_map:
|
|
continue
|
|
if end_index not in feature.token_to_orig_map:
|
|
continue
|
|
if not feature.token_is_max_context.get(start_index, False):
|
|
continue
|
|
if end_index < start_index:
|
|
continue
|
|
length = end_index - start_index + 1
|
|
if length > max_answer_length:
|
|
continue
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=feature_index,
|
|
start_index=start_index,
|
|
end_index=end_index,
|
|
start_logit=result.start_logits[start_index],
|
|
end_logit=result.end_logits[end_index]))
|
|
if version_2_with_negative:
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=min_null_feature_index,
|
|
start_index=0,
|
|
end_index=0,
|
|
start_logit=null_start_logit,
|
|
end_logit=null_end_logit))
|
|
prelim_predictions = sorted(
|
|
prelim_predictions,
|
|
key=lambda x: (x.start_logit + x.end_logit),
|
|
reverse=True)
|
|
|
|
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"NbestPrediction", ["text", "start_logit", "end_logit"])
|
|
|
|
seen_predictions = {}
|
|
nbest = []
|
|
for pred in prelim_predictions:
|
|
if len(nbest) >= n_best_size:
|
|
break
|
|
feature = features[pred.feature_index]
|
|
if pred.start_index > 0: # this is a non-null prediction
|
|
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
|
|
orig_doc_start = feature.token_to_orig_map[pred.start_index]
|
|
orig_doc_end = feature.token_to_orig_map[pred.end_index]
|
|
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
|
|
tok_text = " ".join(tok_tokens)
|
|
|
|
# De-tokenize WordPieces that have been split off.
|
|
tok_text = tok_text.replace(" ##", "")
|
|
tok_text = tok_text.replace("##", "")
|
|
|
|
# Clean whitespace
|
|
tok_text = tok_text.strip()
|
|
tok_text = " ".join(tok_text.split())
|
|
orig_text = " ".join(orig_tokens)
|
|
|
|
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
|
|
if final_text in seen_predictions:
|
|
continue
|
|
|
|
seen_predictions[final_text] = True
|
|
else:
|
|
final_text = ""
|
|
seen_predictions[final_text] = True
|
|
|
|
nbest.append(
|
|
_NbestPrediction(
|
|
text=final_text,
|
|
start_logit=pred.start_logit,
|
|
end_logit=pred.end_logit))
|
|
# if we didn't include the empty option in the n-best, include it
|
|
if version_2_with_negative:
|
|
if "" not in seen_predictions:
|
|
nbest.append(
|
|
_NbestPrediction(
|
|
text="",
|
|
start_logit=null_start_logit,
|
|
end_logit=null_end_logit))
|
|
|
|
# In very rare edge cases we could only have single null prediction.
|
|
# So we just create a nonce prediction in this case to avoid failure.
|
|
if len(nbest) == 1:
|
|
nbest.insert(0,
|
|
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
|
|
|
|
# In very rare edge cases we could have no valid predictions. So we
|
|
# just create a nonce prediction in this case to avoid failure.
|
|
if not nbest:
|
|
nbest.append(
|
|
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
|
|
|
|
assert len(nbest) >= 1
|
|
|
|
total_scores = []
|
|
best_non_null_entry = None
|
|
for entry in nbest:
|
|
total_scores.append(entry.start_logit + entry.end_logit)
|
|
if not best_non_null_entry:
|
|
if entry.text:
|
|
best_non_null_entry = entry
|
|
|
|
probs = _compute_softmax(total_scores)
|
|
|
|
nbest_json = []
|
|
for (i, entry) in enumerate(nbest):
|
|
output = collections.OrderedDict()
|
|
output["text"] = entry.text
|
|
output["probability"] = probs[i]
|
|
output["start_logit"] = entry.start_logit
|
|
output["end_logit"] = entry.end_logit
|
|
nbest_json.append(output)
|
|
|
|
assert len(nbest_json) >= 1
|
|
|
|
if not version_2_with_negative:
|
|
all_predictions[example.qas_id] = nbest_json[0]["text"]
|
|
else:
|
|
# predict "" iff the null score - the score of best non-null > threshold
|
|
score_diff = score_null - best_non_null_entry.start_logit - (
|
|
best_non_null_entry.end_logit)
|
|
scores_diff_json[example.qas_id] = score_diff
|
|
if score_diff > null_score_diff_threshold:
|
|
all_predictions[example.qas_id] = ""
|
|
else:
|
|
all_predictions[example.qas_id] = best_non_null_entry.text
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
|
|
with open(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
with open(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
if version_2_with_negative:
|
|
with open(output_null_log_odds_file, "w") as writer:
|
|
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
|
|
|
|
|
|
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
|
|
"""Project the tokenized prediction back to the original text."""
|
|
|
|
# When we created the data, we kept track of the alignment between original
|
|
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
|
|
# now `orig_text` contains the span of our original text corresponding to the
|
|
# span that we predicted.
|
|
#
|
|
# However, `orig_text` may contain extra characters that we don't want in
|
|
# our prediction.
|
|
#
|
|
# For example, let's say:
|
|
# pred_text = steve smith
|
|
# orig_text = Steve Smith's
|
|
#
|
|
# We don't want to return `orig_text` because it contains the extra "'s".
|
|
#
|
|
# We don't want to return `pred_text` because it's already been normalized
|
|
# (the SQuAD eval script also does punctuation stripping/lower casing but
|
|
# our tokenizer does additional normalization like stripping accent
|
|
# characters).
|
|
#
|
|
# What we really want to return is "Steve Smith".
|
|
#
|
|
# Therefore, we have to apply a semi-complicated alignment heruistic between
|
|
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
|
|
# can fail in certain cases in which case we just return `orig_text`.
|
|
|
|
def _strip_spaces(text):
|
|
ns_chars = []
|
|
ns_to_s_map = collections.OrderedDict()
|
|
for (i, c) in enumerate(text):
|
|
if c == " ":
|
|
continue
|
|
ns_to_s_map[len(ns_chars)] = i
|
|
ns_chars.append(c)
|
|
ns_text = "".join(ns_chars)
|
|
return (ns_text, ns_to_s_map)
|
|
|
|
# We first tokenize `orig_text`, strip whitespace from the result
|
|
# and `pred_text`, and check if they are the same length. If they are
|
|
# NOT the same length, the heuristic has failed. If they are the same
|
|
# length, we assume the characters are one-to-one aligned.
|
|
|
|
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
|
|
|
|
tok_text = " ".join(tokenizer.tokenize(orig_text))
|
|
|
|
start_position = tok_text.find(pred_text)
|
|
if start_position == -1:
|
|
if verbose_logging:
|
|
logger.info(
|
|
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
|
|
return orig_text
|
|
end_position = start_position + len(pred_text) - 1
|
|
|
|
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
|
|
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
|
|
|
|
if len(orig_ns_text) != len(tok_ns_text):
|
|
if verbose_logging:
|
|
logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
|
|
orig_ns_text, tok_ns_text)
|
|
return orig_text
|
|
|
|
# We then project the characters in `pred_text` back to `orig_text` using
|
|
# the character-to-character alignment.
|
|
tok_s_to_ns_map = {}
|
|
for (i, tok_index) in tok_ns_to_s_map.items():
|
|
tok_s_to_ns_map[tok_index] = i
|
|
|
|
orig_start_position = None
|
|
if start_position in tok_s_to_ns_map:
|
|
ns_start_position = tok_s_to_ns_map[start_position]
|
|
if ns_start_position in orig_ns_to_s_map:
|
|
orig_start_position = orig_ns_to_s_map[ns_start_position]
|
|
|
|
if orig_start_position is None:
|
|
if verbose_logging:
|
|
logger.info("Couldn't map start position")
|
|
return orig_text
|
|
|
|
orig_end_position = None
|
|
if end_position in tok_s_to_ns_map:
|
|
ns_end_position = tok_s_to_ns_map[end_position]
|
|
if ns_end_position in orig_ns_to_s_map:
|
|
orig_end_position = orig_ns_to_s_map[ns_end_position]
|
|
|
|
if orig_end_position is None:
|
|
if verbose_logging:
|
|
logger.info("Couldn't map end position")
|
|
return orig_text
|
|
|
|
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
|
|
return output_text
|
|
|
|
|
|
def _get_best_indexes(logits, n_best_size):
|
|
"""Get the n-best logits from a list."""
|
|
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
|
|
|
|
best_indexes = []
|
|
for i in range(len(index_and_score)):
|
|
if i >= n_best_size:
|
|
break
|
|
best_indexes.append(index_and_score[i][0])
|
|
return best_indexes
|
|
|
|
|
|
def _compute_softmax(scores):
|
|
"""Compute softmax probability over raw logits."""
|
|
if not scores:
|
|
return []
|
|
|
|
max_score = None
|
|
for score in scores:
|
|
if max_score is None or score > max_score:
|
|
max_score = score
|
|
|
|
exp_scores = []
|
|
total_sum = 0.0
|
|
for score in scores:
|
|
x = math.exp(score - max_score)
|
|
exp_scores.append(x)
|
|
total_sum += x
|
|
|
|
probs = []
|
|
for score in exp_scores:
|
|
probs.append(score / total_sum)
|
|
return probs
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
## Required parameters
|
|
parser.add_argument("--bert_model", default=None, type=str, required=True,
|
|
help="Bert pre-trained model selected in the list: bert-base-uncased, "
|
|
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
|
|
"bert-base-multilingual-cased, bert-base-chinese.")
|
|
parser.add_argument("--output_dir", default=None, type=str, required=True,
|
|
help="The output directory where the model checkpoints and predictions will be written.")
|
|
parser.add_argument("--init_checkpoint",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The checkpoint file from pretraining")
|
|
|
|
## Other parameters
|
|
parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
|
|
parser.add_argument("--predict_file", default=None, type=str,
|
|
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
|
|
parser.add_argument("--max_seq_length", default=384, type=int,
|
|
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
|
|
"longer than this will be truncated, and sequences shorter than this will be padded.")
|
|
parser.add_argument("--doc_stride", default=128, type=int,
|
|
help="When splitting up a long document into chunks, how much stride to take between chunks.")
|
|
parser.add_argument("--max_query_length", default=64, type=int,
|
|
help="The maximum number of tokens for the question. Questions longer than this will "
|
|
"be truncated to this length.")
|
|
parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
|
|
parser.add_argument("--old", action='store_true', help="use old fp16 optimizer")
|
|
parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.")
|
|
parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
|
|
parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
|
|
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
|
|
parser.add_argument("--num_train_epochs", default=3.0, type=float,
|
|
help="Total number of training epochs to perform.")
|
|
parser.add_argument("--max_steps", default=-1.0, type=float,
|
|
help="Total number of training steps to perform.")
|
|
parser.add_argument("--warmup_proportion", default=0.1, type=float,
|
|
help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
|
|
"of training.")
|
|
parser.add_argument("--n_best_size", default=20, type=int,
|
|
help="The total number of n-best predictions to generate in the nbest_predictions.json "
|
|
"output file.")
|
|
parser.add_argument("--max_answer_length", default=30, type=int,
|
|
help="The maximum length of an answer that can be generated. This is needed because the start "
|
|
"and end predictions are not conditioned on one another.")
|
|
parser.add_argument("--verbose_logging", action='store_true',
|
|
help="If true, all of the warnings related to data processing will be printed. "
|
|
"A number of warnings are expected for a normal SQuAD evaluation.")
|
|
parser.add_argument("--no_cuda",
|
|
action='store_true',
|
|
help="Whether not to use CUDA when available")
|
|
parser.add_argument('--seed',
|
|
type=int,
|
|
default=42,
|
|
help="random seed for initialization")
|
|
parser.add_argument('--gradient_accumulation_steps',
|
|
type=int,
|
|
default=1,
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.")
|
|
parser.add_argument("--do_lower_case",
|
|
action='store_true',
|
|
help="Whether to lower case the input text. True for uncased models, False for cased models.")
|
|
parser.add_argument("--local_rank",
|
|
type=int,
|
|
default=-1,
|
|
help="local_rank for distributed training on gpus")
|
|
parser.add_argument('--fp16',
|
|
action='store_true',
|
|
help="Whether to use 16-bit float precision instead of 32-bit")
|
|
parser.add_argument('--loss_scale',
|
|
type=float, default=0,
|
|
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
|
|
"0 (default value): dynamic loss scaling.\n"
|
|
"Positive power of 2: static loss scaling value.\n")
|
|
parser.add_argument('--version_2_with_negative',
|
|
action='store_true',
|
|
help='If true, the SQuAD examples contain some that do not have an answer.')
|
|
parser.add_argument('--null_score_diff_threshold',
|
|
type=float, default=0.0,
|
|
help="If null_score - best_non_null is greater than the threshold predict null.")
|
|
parser.add_argument('--vocab_file',
|
|
type=str, default=None, required=True,
|
|
help="Vocabulary mapping/file BERT was pretrainined on")
|
|
parser.add_argument("--config_file",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The BERT model config")
|
|
parser.add_argument('--log_freq',
|
|
type=int, default=50,
|
|
help='frequency of logging loss.')
|
|
args = parser.parse_args()
|
|
|
|
if args.local_rank == -1 or args.no_cuda:
|
|
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
|
n_gpu = torch.cuda.device_count()
|
|
else:
|
|
torch.cuda.set_device(args.local_rank)
|
|
device = torch.device("cuda", args.local_rank)
|
|
n_gpu = 1
|
|
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
|
torch.distributed.init_process_group(backend='nccl', init_method='env://')
|
|
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
|
|
device, n_gpu, bool(args.local_rank != -1), args.fp16))
|
|
|
|
if args.gradient_accumulation_steps < 1:
|
|
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
|
|
args.gradient_accumulation_steps))
|
|
|
|
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
|
|
|
|
random.seed(args.seed)
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
if n_gpu > 0:
|
|
torch.cuda.manual_seed_all(args.seed)
|
|
|
|
if not args.do_train and not args.do_predict:
|
|
raise ValueError("At least one of `do_train` or `do_predict` must be True.")
|
|
|
|
if args.do_train:
|
|
if not args.train_file:
|
|
raise ValueError(
|
|
"If `do_train` is True, then `train_file` must be specified.")
|
|
if args.do_predict:
|
|
if not args.predict_file:
|
|
raise ValueError(
|
|
"If `do_predict` is True, then `predict_file` must be specified.")
|
|
|
|
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and os.listdir(args.output_dir)!=['logfile.txt']:
|
|
print("WARNING: Output directory {} already exists and is not empty.".format(args.output_dir), os.listdir(args.output_dir))
|
|
if not os.path.exists(args.output_dir):
|
|
os.makedirs(args.output_dir)
|
|
|
|
tokenizer = BertTokenizer(args.vocab_file, do_lower_case=args.do_lower_case, max_len=512) # for bert large
|
|
# tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
|
|
|
|
train_examples = None
|
|
num_train_optimization_steps = None
|
|
if args.do_train:
|
|
train_examples = read_squad_examples(
|
|
input_file=args.train_file, is_training=True, version_2_with_negative=args.version_2_with_negative)
|
|
num_train_optimization_steps = int(
|
|
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
|
|
if args.local_rank != -1:
|
|
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
|
|
|
|
# Prepare model
|
|
config = BertConfig.from_json_file(args.config_file)
|
|
model = BertForQuestionAnswering(config)
|
|
# model = BertForQuestionAnswering.from_pretrained(args.bert_model,
|
|
# cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)))
|
|
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'), strict=False)
|
|
|
|
model.to(device)
|
|
if args.fp16 and args.old:
|
|
model.half()
|
|
# 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 fused_adam_local import FusedAdamBert as FusedAdam
|
|
from apex.optimizers import FusedAdam
|
|
from apex.optimizers import FP16_Optimizer
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
|
|
# import ipdb; ipdb.set_trace()
|
|
optimizer = FusedAdam(optimizer_grouped_parameters,
|
|
lr=args.learning_rate,
|
|
bias_correction=False,
|
|
max_grad_norm=1.0)
|
|
|
|
if args.loss_scale == 0:
|
|
if args.old:
|
|
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
|
|
else:
|
|
model, optimizer = amp.initialize(model, optimizer, opt_level="O2", keep_batchnorm_fp32=False,
|
|
loss_scale="dynamic")
|
|
else:
|
|
if args.old:
|
|
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
|
|
else:
|
|
model, optimizer = amp.initialize(model, optimizer, opt_level="O2", keep_batchnorm_fp32=False, loss_scale=args.loss_scale)
|
|
if not args.old and args.do_train:
|
|
scheduler = LinearWarmUpScheduler(optimizer, warmup=args.warmup_proportion, total_steps=num_train_optimization_steps)
|
|
|
|
else:
|
|
optimizer = BertAdam(optimizer_grouped_parameters,
|
|
lr=args.learning_rate,
|
|
warmup=args.warmup_proportion,
|
|
t_total=num_train_optimization_steps)
|
|
|
|
#print(model)
|
|
if args.local_rank != -1:
|
|
try:
|
|
from apex.parallel import DistributedDataParallel as DDP
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
|
|
|
|
model = DDP(model)
|
|
elif n_gpu > 1:
|
|
model = torch.nn.DataParallel(model)
|
|
|
|
global_step = 0
|
|
if args.do_train:
|
|
cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}'.format(
|
|
list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(args.doc_stride),
|
|
str(args.max_query_length))
|
|
train_features = None
|
|
try:
|
|
with open(cached_train_features_file, "rb") as reader:
|
|
train_features = pickle.load(reader)
|
|
except:
|
|
train_features = convert_examples_to_features(
|
|
examples=train_examples,
|
|
tokenizer=tokenizer,
|
|
max_seq_length=args.max_seq_length,
|
|
doc_stride=args.doc_stride,
|
|
max_query_length=args.max_query_length,
|
|
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)
|
|
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)
|
|
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)
|
|
|
|
model.train()
|
|
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
|
|
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
|
|
# Terminate early for benchmarking
|
|
|
|
if args.max_steps > 0 and global_step > args.max_steps:
|
|
break
|
|
|
|
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:
|
|
if args.old:
|
|
optimizer.backward(loss)
|
|
else:
|
|
with amp.scale_loss(loss, optimizer) as scaled_loss:
|
|
scaled_loss.backward()
|
|
else:
|
|
loss.backward()
|
|
# 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 for BERT which FusedAdam doesn't do
|
|
if not args.old:
|
|
scheduler.step()
|
|
else:
|
|
lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
|
|
for param_group in optimizer.param_groups:
|
|
param_group['lr'] = lr_this_step
|
|
|
|
optimizer.step()
|
|
optimizer.zero_grad()
|
|
global_step += 1
|
|
if step % args.log_freq == 0:
|
|
# logger.info("Step {}: Loss {}, LR {} ".format(global_step, loss.item(), lr_this_step))
|
|
logger.info(
|
|
"Step {}: Loss {}, LR {} ".format(global_step, loss.item(), optimizer.param_groups[0]['lr']))
|
|
|
|
if args.do_train:
|
|
# Save a trained model and the associated configuration
|
|
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
|
|
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
|
|
torch.save(model_to_save.state_dict(), output_model_file)
|
|
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
|
|
with open(output_config_file, 'w') as f:
|
|
f.write(model_to_save.config.to_json_string())
|
|
|
|
# # Load a trained model and config that you have fine-tuned
|
|
# config = BertConfig(output_config_file)
|
|
# model = BertForQuestionAnswering(config)
|
|
# model.load_state_dict(torch.load(output_model_file))
|
|
# else:
|
|
# model = BertForQuestionAnswering.from_pretrained(args.bert_model)
|
|
|
|
|
|
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"):
|
|
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()
|