[examples] Add trainer support for question-answering (#4829)
* add SquadDataset * add DataCollatorForQuestionAnswering * update __init__ * add run_squad with trainer * add DataCollatorForQuestionAnswering in __init__ * pass data_collator to trainer * doc tweak * Update run_squad_trainer.py * Update __init__.py * Update __init__.py Co-authored-by: Julien Chaumond <chaumond@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Suraj Patil
@@ -4,3 +4,4 @@
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from .glue import GlueDataset, GlueDataTrainingArguments
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from .language_modeling import LineByLineTextDataset, TextDataset
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from .squad import SquadDataset, SquadDataTrainingArguments
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189
src/transformers/data/datasets/squad.py
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189
src/transformers/data/datasets/squad.py
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import logging
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import os
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import time
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Dict, List, Optional, Union
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import torch
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from filelock import FileLock
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from torch.utils.data.dataset import Dataset
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from ...modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
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from ...tokenization_utils import PreTrainedTokenizer
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from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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@dataclass
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class SquadDataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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model_type: str = field(
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default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)}
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)
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data_dir: str = field(
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default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
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)
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max_seq_length: int = field(
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default=128,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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doc_stride: int = field(
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default=128,
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metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
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)
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max_query_length: int = field(
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default=64,
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metadata={
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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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},
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)
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max_answer_length: int = field(
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default=30,
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metadata={
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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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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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version_2_with_negative: bool = field(
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default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
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)
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null_score_diff_threshold: float = field(
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default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
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)
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n_best_size: int = field(
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default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
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)
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lang_id: int = field(
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default=0,
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metadata={
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"help": "language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
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},
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)
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threads: int = field(default=1, metadata={"help": "multiple threads for converting example to features"})
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class Split(Enum):
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train = "train"
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dev = "dev"
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class SquadDataset(Dataset):
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"""
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This will be superseded by a framework-agnostic approach
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soon.
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"""
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args: SquadDataTrainingArguments
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features: List[SquadFeatures]
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mode: Split
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is_language_sensitive: bool
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def __init__(
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self,
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args: SquadDataTrainingArguments,
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tokenizer: PreTrainedTokenizer,
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limit_length: Optional[int] = None,
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mode: Union[str, Split] = Split.train,
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is_language_sensitive: Optional[bool] = False,
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cache_dir: Optional[str] = None,
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):
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self.args = args
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self.is_language_sensitive = is_language_sensitive
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self.processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
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if isinstance(mode, str):
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try:
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mode = Split[mode]
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except KeyError:
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raise KeyError("mode is not a valid split name")
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self.mode = mode
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# Load data features from cache or dataset file
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cached_features_file = os.path.join(
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cache_dir if cache_dir is not None else args.data_dir,
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"cached_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(args.max_seq_length),),
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)
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# Make sure only the first process in distributed training processes the dataset,
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# and the others will use the cache.
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lock_path = cached_features_file + ".lock"
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with FileLock(lock_path):
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if os.path.exists(cached_features_file) and not args.overwrite_cache:
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start = time.time()
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self.features = torch.load(cached_features_file)
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logger.info(
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f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
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)
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else:
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if mode == Split.dev:
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examples = self.processor.get_dev_examples(args.data_dir)
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else:
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examples = self.processor.get_train_examples(args.data_dir)
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self.features = squad_convert_examples_to_features(
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examples=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=mode == Split.train,
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threads=args.threads,
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)
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start = time.time()
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torch.save(self.features, cached_features_file)
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# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
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logger.info(
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"Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
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)
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def __len__(self):
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return len(self.features)
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def __getitem__(self, i) -> Dict[str, torch.Tensor]:
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# Convert to Tensors and build dataset
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feature = self.features[i]
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input_ids = torch.tensor(feature.input_ids, dtype=torch.long)
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attention_mask = torch.tensor(feature.attention_mask, dtype=torch.long)
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token_type_ids = torch.tensor(feature.token_type_ids, dtype=torch.long)
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cls_index = torch.tensor(feature.cls_index, dtype=torch.long)
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p_mask = torch.tensor(feature.p_mask, dtype=torch.float)
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is_impossible = torch.tensor(feature.is_impossible, dtype=torch.float)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids,
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}
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if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
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del inputs["token_type_ids"]
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if self.args.model_type in ["xlnet", "xlm"]:
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inputs.update({"cls_index": cls_index, "p_mask": p_mask})
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if self.args.version_2_with_negative:
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inputs.update({"is_impossible": is_impossible})
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if self.is_language_sensitive:
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inputs.update({"langs": (torch.ones(input_ids.shape, dtype=torch.int64) * self.args.lang_id)})
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if self.mode == Split.train:
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start_positions = torch.tensor(feature.start_position, dtype=torch.long)
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end_positions = torch.tensor(feature.end_position, dtype=torch.long)
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inputs.update({"start_positions": start_positions, "end_positions": end_positions})
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return inputs
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