From be6529a7f5a17fd8c602f53e2bcbfdc9cfad93b3 Mon Sep 17 00:00:00 2001 From: kubapok Date: Thu, 11 Nov 2021 10:49:15 +0100 Subject: [PATCH] inference on cuda --- how_to_run | 14 + just_roberta_base/04_predict.py | 24 + just_roberta_base/run.sh | 1 + .../00_create_datasets.py | 30 + regular_roberta_from_scratch/04_predict.py | 24 + .../1_train_tokenizer.py | 18 + regular_roberta_from_scratch/2_run.sh | 22 + .../roberta_small_config.json | 27 + regular_roberta_from_scratch/run.sh | 4 + regular_roberta_from_scratch/run_mlm.py | 556 ++++++++++++++++++ .../tokenizer_config.json | 1 + .../tokenizer_model/.gitkeep | 0 .../tokenizer_model/tokenizer_config.json | 1 + roberta_ft/00_create_datasets.py | 30 + roberta_ft/04_predict.py | 25 + roberta_ft/2_run.sh | 21 + roberta_ft/run.sh | 3 + roberta_ft/run_mlm.py | 552 +++++++++++++++++ .../00_create_datasets.py | 32 + roberta_with_year_from_scratch/04_predict.py | 25 + .../1_train_tokenizer.py | 18 + roberta_with_year_from_scratch/2_run.sh | 22 + roberta_with_year_from_scratch/run.sh | 4 + roberta_with_year_from_scratch/run_mlm.py | 556 ++++++++++++++++++ .../tokenizer_config.json | 1 + .../tokenizer_model/.gitkeep | 0 .../tokenizer_model/tokenizer_config.json | 1 + roberta_with_year_ft/00_create_datasets.py | 32 + roberta_with_year_ft/04_predict.py | 25 + roberta_with_year_ft/2_run.sh | 21 + roberta_with_year_ft/run.sh | 3 + roberta_with_year_ft/run_mlm.py | 552 +++++++++++++++++ 32 files changed, 2645 insertions(+) create mode 100644 how_to_run create mode 100644 just_roberta_base/04_predict.py create mode 100644 just_roberta_base/run.sh create mode 100644 regular_roberta_from_scratch/00_create_datasets.py create mode 100644 regular_roberta_from_scratch/04_predict.py create mode 100644 regular_roberta_from_scratch/1_train_tokenizer.py create mode 100644 regular_roberta_from_scratch/2_run.sh create mode 100644 regular_roberta_from_scratch/roberta_small_config.json create mode 100644 regular_roberta_from_scratch/run.sh create mode 100755 regular_roberta_from_scratch/run_mlm.py create mode 100644 regular_roberta_from_scratch/tokenizer_config.json create mode 100644 regular_roberta_from_scratch/tokenizer_model/.gitkeep create mode 100644 regular_roberta_from_scratch/tokenizer_model/tokenizer_config.json create mode 100644 roberta_ft/00_create_datasets.py create mode 100644 roberta_ft/04_predict.py create mode 100644 roberta_ft/2_run.sh create mode 100644 roberta_ft/run.sh create mode 100755 roberta_ft/run_mlm.py create mode 100644 roberta_with_year_from_scratch/00_create_datasets.py create mode 100644 roberta_with_year_from_scratch/04_predict.py create mode 100644 roberta_with_year_from_scratch/1_train_tokenizer.py create mode 100644 roberta_with_year_from_scratch/2_run.sh create mode 100644 roberta_with_year_from_scratch/run.sh create mode 100755 roberta_with_year_from_scratch/run_mlm.py create mode 100644 roberta_with_year_from_scratch/tokenizer_config.json create mode 100644 roberta_with_year_from_scratch/tokenizer_model/.gitkeep create mode 100644 roberta_with_year_from_scratch/tokenizer_model/tokenizer_config.json create mode 100644 roberta_with_year_ft/00_create_datasets.py create mode 100644 roberta_with_year_ft/04_predict.py create mode 100644 roberta_with_year_ft/2_run.sh create mode 100644 roberta_with_year_ft/run.sh create mode 100755 roberta_with_year_ft/run_mlm.py diff --git a/how_to_run b/how_to_run new file mode 100644 index 0000000..a824979 --- /dev/null +++ b/how_to_run @@ -0,0 +1,14 @@ +install pytorch +install transformers from source: pip install git+https://github.com/huggingface/transformers +instal datasets: pip install datasets +install tokenizers: pip install tokenizers + +to run: go do a solution directory and: bash run.sh + + +adjust: +per_device_train_batch_size +per_device_eval_batch_size +gradient_accumulation_steps + +if you want to run scripts on other dataset remeber to adjust max_seq_length diff --git a/just_roberta_base/04_predict.py b/just_roberta_base/04_predict.py new file mode 100644 index 0000000..ea254f3 --- /dev/null +++ b/just_roberta_base/04_predict.py @@ -0,0 +1,24 @@ +from tqdm import tqdm +from transformers import pipeline + +def get_formatted(text): + answers = unmasker(text, top_k=15) + answers = {x['token_str']:x['score'] for x in answers} + empty = 1 - sum(answers.values()) + answers[''] = empty + answers_str ='' + for k,v in answers.items(): + answers_str += k.strip()+':'+str(v) + ' ' + return answers_str.rstrip(' ').lstrip(' ') + +def write(f_path_in, f_path_out): + with open(f_path_in) as f_in, open(f_path_out,'w') as f_out: + for line in tqdm(f_in,total=150_000): + l_in = line.rstrip().split('\t')[2] + a = get_formatted(l_in) + f_out.write(a + '\n') + +model = 'roberta-base' +unmasker = pipeline('fill-mask', model=model, device=0) +write('../dev-0/in.tsv', '../dev-0/out.tsv') +write('../test-A/in.tsv', '../test-A/out.tsv') diff --git a/just_roberta_base/run.sh b/just_roberta_base/run.sh new file mode 100644 index 0000000..8c156ce --- /dev/null +++ b/just_roberta_base/run.sh @@ -0,0 +1 @@ +python 04_predict.py diff --git a/regular_roberta_from_scratch/00_create_datasets.py b/regular_roberta_from_scratch/00_create_datasets.py new file mode 100644 index 0000000..b2433c4 --- /dev/null +++ b/regular_roberta_from_scratch/00_create_datasets.py @@ -0,0 +1,30 @@ +import datetime + +with open('../test-A/in.tsv','r') as f_in, open(f'./test-A_in.csv', 'w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont, date, text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + f_hf.write(text +'\n') + + +for dataset in 'train', 'dev-0': + with open(f'../{dataset}/in.tsv') as f_in, open(f'./{dataset}_in.csv','w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont,date,text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + f_hf.write(text +'\n') + diff --git a/regular_roberta_from_scratch/04_predict.py b/regular_roberta_from_scratch/04_predict.py new file mode 100644 index 0000000..5d08ea5 --- /dev/null +++ b/regular_roberta_from_scratch/04_predict.py @@ -0,0 +1,24 @@ +from tqdm import tqdm +from transformers import pipeline + +def get_formatted(text): + answers = unmasker(text, top_k=15) + answers = {x['token_str']:x['score'] for x in answers} + empty = 1 - sum(answers.values()) + answers[''] = empty + answers_str ='' + for k,v in answers.items(): + answers_str += k.strip()+':'+str(v) + ' ' + return answers_str.rstrip(' ').lstrip(' ') + +def write(f_path_in, f_path_out): + with open(f_path_in) as f_in, open(f_path_out,'w') as f_out: + for line in tqdm(f_in,total=150_000): + l_in = line.rstrip().split('\t')[2] + a = get_formatted(l_in) + f_out.write(a + '\n') + +model = 'robertamodel' +unmasker = pipeline('fill-mask', model=model, device=0) +write('../dev-0/in.tsv', '../dev-0/out.tsv') +write('../test-A/in.tsv', '../test-A/out.tsv') diff --git a/regular_roberta_from_scratch/1_train_tokenizer.py b/regular_roberta_from_scratch/1_train_tokenizer.py new file mode 100644 index 0000000..5b8c980 --- /dev/null +++ b/regular_roberta_from_scratch/1_train_tokenizer.py @@ -0,0 +1,18 @@ +from pathlib import Path +from tokenizers import ByteLevelBPETokenizer + +paths = ['./train_in.csv'] + +# Initialize a tokenizer +tokenizer = ByteLevelBPETokenizer() + +# Customize training +tokenizer.train(files=paths, vocab_size=50265, min_frequency=2, special_tokens=[ + "", + "", + "", + "", + "", +]) + +tokenizer.save_model("./tokenizer_model") diff --git a/regular_roberta_from_scratch/2_run.sh b/regular_roberta_from_scratch/2_run.sh new file mode 100644 index 0000000..cde527f --- /dev/null +++ b/regular_roberta_from_scratch/2_run.sh @@ -0,0 +1,22 @@ +#if [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then echo "CUDA_VISIBLE_DEVICES NOT SET"; exit 1 ; else echo "using cuda devices '$CUDA_VISIBLE_DEVICES'"; fi + +python run_mlm.py \ + --model_name_or_path roberta-base \ + --max_seq_length 64 \ + --output_dir ./robertamodel \ + --train_file ./train_in.csv \ + --validation_file ./dev-0_in.csv \ + --do_train \ + --do_eval \ + --per_device_train_batch_size=256 \ + --per_device_eval_batch_size=256 \ + --gradient_accumulation_steps=1 \ + --fp16 False \ + --save_steps 1000 \ + --eval_steps 1000 \ + --logging_steps 1000 \ + --evaluation_strategy steps \ + --num_train_epochs 40 \ + --warmup_steps 1000 \ + --tokenizer_name tokenizer_model \ + | tee --append logs_regular.txt diff --git a/regular_roberta_from_scratch/roberta_small_config.json b/regular_roberta_from_scratch/roberta_small_config.json new file mode 100644 index 0000000..6866bbc --- /dev/null +++ b/regular_roberta_from_scratch/roberta_small_config.json @@ -0,0 +1,27 @@ +{ + "architectures": [ + "RobertaForMaskedLM" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "classifier_dropout": null, + "eos_token_id": 2, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 66, + "model_type": "roberta", + "num_attention_heads": 12, + "num_hidden_layers": 6, + "pad_token_id": 1, + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.11.0.dev0", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 50265 +} diff --git a/regular_roberta_from_scratch/run.sh b/regular_roberta_from_scratch/run.sh new file mode 100644 index 0000000..c1cc19d --- /dev/null +++ b/regular_roberta_from_scratch/run.sh @@ -0,0 +1,4 @@ +python 00_create_datasets.py +python 1_train_tokenizer.py +bash 2_run.sh +python 04_predict.py diff --git a/regular_roberta_from_scratch/run_mlm.py b/regular_roberta_from_scratch/run_mlm.py new file mode 100755 index 0000000..e3e89dc --- /dev/null +++ b/regular_roberta_from_scratch/run_mlm.py @@ -0,0 +1,556 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2020 The HuggingFace Team All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset. + +Here is the full list of checkpoints on the hub that can be fine-tuned by this script: +https://huggingface.co/models?filter=masked-lm +""" +# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. + +import logging +import math +import os +import sys +from dataclasses import dataclass, field +from typing import Optional + +import datasets +from datasets import load_dataset + +import transformers +from transformers import ( + CONFIG_MAPPING, + MODEL_FOR_MASKED_LM_MAPPING, + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + HfArgumentParser, + Trainer, + TrainingArguments, + set_seed, +) +from transformers.trainer_utils import get_last_checkpoint +from transformers.utils import check_min_version +from transformers.utils.versions import require_version + + +# Will error if the minimal version of Transformers is not installed. Remove at your own risks. +check_min_version("4.12.0.dev0") + +require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") + +logger = logging.getLogger(__name__) +MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) +MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) + + +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. + """ + + model_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The model checkpoint for weights initialization." + "Don't set if you want to train a model from scratch." + }, + ) + model_type: Optional[str] = field( + default=None, + metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, + ) + config_overrides: Optional[str] = field( + default=None, + metadata={ + "help": "Override some existing default config settings when a model is trained from scratch. Example: " + "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" + }, + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + model_revision: str = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, + ) + use_auth_token: bool = field( + default=False, + metadata={ + "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script " + "with private models)." + }, + ) + + def __post_init__(self): + if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): + raise ValueError( + "--config_overrides can't be used in combination with --config_name or --model_name_or_path" + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + dataset_name: Optional[str] = field( + default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: Optional[str] = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) + validation_file: Optional[str] = field( + default=None, + metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + validation_split_percentage: Optional[int] = field( + default=5, + metadata={ + "help": "The percentage of the train set used as validation set in case there's no validation split" + }, + ) + max_seq_length: Optional[int] = field( + default=None, + metadata={ + "help": "The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated." + }, + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + mlm_probability: float = field( + default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} + ) + line_by_line: bool = field( + default=False, + metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, + ) + pad_to_max_length: bool = field( + default=False, + metadata={ + "help": "Whether to pad all samples to `max_seq_length`. " + "If False, will pad the samples dynamically when batching to the maximum length in the batch." + }, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + }, + ) + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." + + +def main(): + # See all possible arguments in src/transformers/training_args.py + # or by passing the --help flag to this script. + # We now keep distinct sets of args, for a cleaner separation of concerns. + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + # Setup logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + + log_level = training_args.get_process_log_level() + logger.setLevel(log_level) + datasets.utils.logging.set_verbosity(log_level) + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_default_handler() + transformers.utils.logging.enable_explicit_format() + + # Log on each process the small summary: + logger.warning( + f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + # Set the verbosity to info of the Transformers logger (on main process only): + logger.info(f"Training/evaluation parameters {training_args}") + + # Detecting last checkpoint. + last_checkpoint = None + if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: + last_checkpoint = get_last_checkpoint(training_args.output_dir) + if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: + raise ValueError( + f"Output directory ({training_args.output_dir}) already exists and is not empty. " + "Use --overwrite_output_dir to overcome." + ) + elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: + logger.info( + f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " + "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." + ) + + # Set seed before initializing model. + set_seed(training_args.seed) + + # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) + # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ + # (the dataset will be downloaded automatically from the datasets Hub + # + # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this + # behavior (see below) + # + # In distributed training, the load_dataset function guarantee that only one local process can concurrently + # download the dataset. + if data_args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + raw_datasets = load_dataset( + data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir + ) + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + else: + data_files = {} + if data_args.train_file is not None: + data_files["train"] = data_args.train_file + extension = data_args.train_file.split(".")[-1] + if data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.validation_file.split(".")[-1] + if extension == "txt": + extension = "text" + raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, sep='\t') + + # If no validation data is there, validation_split_percentage will be used to divide the dataset. + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + extension, + data_files=data_files, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + extension, + data_files=data_files, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + + # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at + # https://huggingface.co/docs/datasets/loading_datasets.html. + + # Load pretrained model and tokenizer + # + # Distributed training: + # The .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + config_kwargs = { + "cache_dir": model_args.cache_dir, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.config_name: + config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) + elif model_args.model_name_or_path: + config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) + else: + config = CONFIG_MAPPING[model_args.model_type]() + logger.warning("You are instantiating a new config instance from scratch.") + if model_args.config_overrides is not None: + logger.info(f"Overriding config: {model_args.config_overrides}") + config.update_from_string(model_args.config_overrides) + + tokenizer_kwargs = { + "cache_dir": model_args.cache_dir, + "use_fast": model_args.use_fast_tokenizer, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.tokenizer_name: + tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) + elif model_args.model_name_or_path: + tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) + else: + raise ValueError( + "You are instantiating a new tokenizer from scratch. This is not supported by this script." + "You can do it from another script, save it, and load it from here, using --tokenizer_name." + ) + + if model_args.model_name_or_path: + model = AutoModelForMaskedLM.from_pretrained( + model_args.model_name_or_path, + from_tf=bool(".ckpt" in model_args.model_name_or_path), + config=config, + cache_dir=model_args.cache_dir, + revision=model_args.model_revision, + use_auth_token=True if model_args.use_auth_token else None, + ) + from transformers import RobertaTokenizer, RobertaForMaskedLM, RobertaConfig + config = model.config + model = RobertaForMaskedLM(config) + + else: + logger.info("Training new model from scratch") + model = AutoModelForMaskedLM.from_config(config) + + model.resize_token_embeddings(len(tokenizer)) + + # Preprocessing the datasets. + # First we tokenize all the texts. + if training_args.do_train: + column_names = raw_datasets["train"].column_names + else: + column_names = raw_datasets["validation"].column_names + text_column_name = "text" if "text" in column_names else column_names[0] + + if data_args.max_seq_length is None: + max_seq_length = tokenizer.model_max_length + if max_seq_length > 1024: + logger.warning( + f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " + "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." + ) + max_seq_length = 1024 + else: + if data_args.max_seq_length > tokenizer.model_max_length: + logger.warning( + f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" + f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." + ) + max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) + + if data_args.line_by_line: + # When using line_by_line, we just tokenize each nonempty line. + padding = "max_length" if data_args.pad_to_max_length else False + + def tokenize_function(examples): + # Remove empty lines + examples[text_column_name] = [ + line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() + ] + return tokenizer( + examples[text_column_name], + padding=padding, + truncation=True, + max_length=max_seq_length, + # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it + # receives the `special_tokens_mask`. + return_special_tokens_mask=True, + ) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=[text_column_name], + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on dataset line_by_line", + ) + else: + # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. + # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more + # efficient when it receives the `special_tokens_mask`. + def tokenize_function(examples): + return tokenizer(examples[text_column_name], return_special_tokens_mask=True) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on every text in dataset", + ) + + # Main data processing function that will concatenate all texts from our dataset and generate chunks of + # max_seq_length. + def group_texts(examples): + # Concatenate all texts. + concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} + total_length = len(concatenated_examples[list(examples.keys())[0]]) + # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can + # customize this part to your needs. + if total_length >= max_seq_length: + total_length = (total_length // max_seq_length) * max_seq_length + # Split by chunks of max_len. + result = { + k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] + for k, t in concatenated_examples.items() + } + return result + + # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a + # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value + # might be slower to preprocess. + # + # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: + # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map + + with training_args.main_process_first(desc="grouping texts together"): + tokenized_datasets = tokenized_datasets.map( + group_texts, + batched=True, + num_proc=data_args.preprocessing_num_workers, + load_from_cache_file=not data_args.overwrite_cache, + desc=f"Grouping texts in chunks of {max_seq_length}", + ) + + if training_args.do_train: + if "train" not in tokenized_datasets: + raise ValueError("--do_train requires a train dataset") + train_dataset = tokenized_datasets["train"] + if data_args.max_train_samples is not None: + train_dataset = train_dataset.select(range(data_args.max_train_samples)) + + if training_args.do_eval: + if "validation" not in tokenized_datasets: + raise ValueError("--do_eval requires a validation dataset") + eval_dataset = tokenized_datasets["validation"] + if data_args.max_eval_samples is not None: + eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) + + # Data collator + # This one will take care of randomly masking the tokens. + pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length + data_collator = DataCollatorForLanguageModeling( + tokenizer=tokenizer, + mlm_probability=data_args.mlm_probability, + pad_to_multiple_of=8 if pad_to_multiple_of_8 else None, + ) + + # Initialize our Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset if training_args.do_train else None, + eval_dataset=eval_dataset if training_args.do_eval else None, + tokenizer=tokenizer, + data_collator=data_collator, + ) + + # Training + if training_args.do_train: + checkpoint = None + if training_args.resume_from_checkpoint is not None: + checkpoint = training_args.resume_from_checkpoint + elif last_checkpoint is not None: + checkpoint = last_checkpoint + train_result = trainer.train(resume_from_checkpoint=checkpoint) + trainer.save_model() # Saves the tokenizer too for easy upload + metrics = train_result.metrics + + max_train_samples = ( + data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) + ) + metrics["train_samples"] = min(max_train_samples, len(train_dataset)) + + trainer.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + + # Evaluation + if training_args.do_eval: + logger.info("*** Evaluate ***") + + metrics = trainer.evaluate() + + max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) + metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) + try: + perplexity = math.exp(metrics["eval_loss"]) + except OverflowError: + perplexity = float("inf") + metrics["perplexity"] = perplexity + + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"} + if data_args.dataset_name is not None: + kwargs["dataset_tags"] = data_args.dataset_name + if data_args.dataset_config_name is not None: + kwargs["dataset_args"] = data_args.dataset_config_name + kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" + else: + kwargs["dataset"] = data_args.dataset_name + + if training_args.push_to_hub: + trainer.push_to_hub(**kwargs) + else: + trainer.create_model_card(**kwargs) + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main() diff --git a/regular_roberta_from_scratch/tokenizer_config.json b/regular_roberta_from_scratch/tokenizer_config.json new file mode 100644 index 0000000..cbc6ef8 --- /dev/null +++ b/regular_roberta_from_scratch/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": "", "bos_token": "", "eos_token": "", "add_prefix_space": false, "errors": "replace", "sep_token": "", "cls_token": "", "pad_token": "", "mask_token": "", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"} \ No newline at end of file diff --git a/regular_roberta_from_scratch/tokenizer_model/.gitkeep b/regular_roberta_from_scratch/tokenizer_model/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/regular_roberta_from_scratch/tokenizer_model/tokenizer_config.json b/regular_roberta_from_scratch/tokenizer_model/tokenizer_config.json new file mode 100644 index 0000000..cbc6ef8 --- /dev/null +++ b/regular_roberta_from_scratch/tokenizer_model/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": "", "bos_token": "", "eos_token": "", "add_prefix_space": false, "errors": "replace", "sep_token": "", "cls_token": "", "pad_token": "", "mask_token": "", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"} \ No newline at end of file diff --git a/roberta_ft/00_create_datasets.py b/roberta_ft/00_create_datasets.py new file mode 100644 index 0000000..b2433c4 --- /dev/null +++ b/roberta_ft/00_create_datasets.py @@ -0,0 +1,30 @@ +import datetime + +with open('../test-A/in.tsv','r') as f_in, open(f'./test-A_in.csv', 'w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont, date, text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + f_hf.write(text +'\n') + + +for dataset in 'train', 'dev-0': + with open(f'../{dataset}/in.tsv') as f_in, open(f'./{dataset}_in.csv','w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont,date,text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + f_hf.write(text +'\n') + diff --git a/roberta_ft/04_predict.py b/roberta_ft/04_predict.py new file mode 100644 index 0000000..1634df1 --- /dev/null +++ b/roberta_ft/04_predict.py @@ -0,0 +1,25 @@ +from tqdm import tqdm +from transformers import pipeline + +def get_formatted(text): + answers = unmasker(text, top_k=15) + answers = {x['token_str']:x['score'] for x in answers} + empty = 1 - sum(answers.values()) + answers[''] = empty + answers_str ='' + for k,v in answers.items(): + answers_str += k.strip()+':'+str(v) + ' ' + return answers_str.rstrip(' ').lstrip(' ') + +def write(f_path_in, f_path_out): + with open(f_path_in) as f_in, open(f_path_out,'w') as f_out: + next(f_in) + for line in tqdm(f_in,total=150_000): + l_in = line.rstrip() + a = get_formatted(l_in) + f_out.write(a + '\n') + +model = 'robertamodel' +unmasker = pipeline('fill-mask', model=model, device=0) +write('dev-0_in.csv', '../dev-0/out.tsv') +write('test-A_in.csv', '../test-A/out.tsv') diff --git a/roberta_ft/2_run.sh b/roberta_ft/2_run.sh new file mode 100644 index 0000000..bb9c281 --- /dev/null +++ b/roberta_ft/2_run.sh @@ -0,0 +1,21 @@ +#if [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then echo "CUDA_VISIBLE_DEVICES NOT SET"; exit 1 ; else echo "using cuda devices '$CUDA_VISIBLE_DEVICES'"; fi + +python run_mlm.py \ + --model_name_or_path roberta-base \ + --max_seq_length 64 \ + --output_dir ./robertamodel \ + --train_file ./train_in.csv \ + --validation_file ./dev-0_in.csv \ + --do_train \ + --do_eval \ + --per_device_train_batch_size=256 \ + --per_device_eval_batch_size=256 \ + --gradient_accumulation_steps=1 \ + --fp16 False \ + --save_steps 1000 \ + --eval_steps 1000 \ + --logging_steps 1000 \ + --evaluation_strategy steps \ + --num_train_epochs 40 \ + --warmup_steps 1000 \ + | tee --append logs_regular.txt diff --git a/roberta_ft/run.sh b/roberta_ft/run.sh new file mode 100644 index 0000000..a35e620 --- /dev/null +++ b/roberta_ft/run.sh @@ -0,0 +1,3 @@ +python 00_create_datasets.py +bash 2_run.sh +python 04_predict.py diff --git a/roberta_ft/run_mlm.py b/roberta_ft/run_mlm.py new file mode 100755 index 0000000..306a3ce --- /dev/null +++ b/roberta_ft/run_mlm.py @@ -0,0 +1,552 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2020 The HuggingFace Team All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset. + +Here is the full list of checkpoints on the hub that can be fine-tuned by this script: +https://huggingface.co/models?filter=masked-lm +""" +# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. + +import logging +import math +import os +import sys +from dataclasses import dataclass, field +from typing import Optional + +import datasets +from datasets import load_dataset + +import transformers +from transformers import ( + CONFIG_MAPPING, + MODEL_FOR_MASKED_LM_MAPPING, + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + HfArgumentParser, + Trainer, + TrainingArguments, + set_seed, +) +from transformers.trainer_utils import get_last_checkpoint +from transformers.utils import check_min_version +from transformers.utils.versions import require_version + + +# Will error if the minimal version of Transformers is not installed. Remove at your own risks. +check_min_version("4.12.0.dev0") + +require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") + +logger = logging.getLogger(__name__) +MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) +MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) + + +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. + """ + + model_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The model checkpoint for weights initialization." + "Don't set if you want to train a model from scratch." + }, + ) + model_type: Optional[str] = field( + default=None, + metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, + ) + config_overrides: Optional[str] = field( + default=None, + metadata={ + "help": "Override some existing default config settings when a model is trained from scratch. Example: " + "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" + }, + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + model_revision: str = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, + ) + use_auth_token: bool = field( + default=False, + metadata={ + "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script " + "with private models)." + }, + ) + + def __post_init__(self): + if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): + raise ValueError( + "--config_overrides can't be used in combination with --config_name or --model_name_or_path" + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + dataset_name: Optional[str] = field( + default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: Optional[str] = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) + validation_file: Optional[str] = field( + default=None, + metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + validation_split_percentage: Optional[int] = field( + default=5, + metadata={ + "help": "The percentage of the train set used as validation set in case there's no validation split" + }, + ) + max_seq_length: Optional[int] = field( + default=None, + metadata={ + "help": "The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated." + }, + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + mlm_probability: float = field( + default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} + ) + line_by_line: bool = field( + default=False, + metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, + ) + pad_to_max_length: bool = field( + default=False, + metadata={ + "help": "Whether to pad all samples to `max_seq_length`. " + "If False, will pad the samples dynamically when batching to the maximum length in the batch." + }, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + }, + ) + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." + + +def main(): + # See all possible arguments in src/transformers/training_args.py + # or by passing the --help flag to this script. + # We now keep distinct sets of args, for a cleaner separation of concerns. + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + # Setup logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + + log_level = training_args.get_process_log_level() + logger.setLevel(log_level) + datasets.utils.logging.set_verbosity(log_level) + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_default_handler() + transformers.utils.logging.enable_explicit_format() + + # Log on each process the small summary: + logger.warning( + f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + # Set the verbosity to info of the Transformers logger (on main process only): + logger.info(f"Training/evaluation parameters {training_args}") + + # Detecting last checkpoint. + last_checkpoint = None + if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: + last_checkpoint = get_last_checkpoint(training_args.output_dir) + if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: + raise ValueError( + f"Output directory ({training_args.output_dir}) already exists and is not empty. " + "Use --overwrite_output_dir to overcome." + ) + elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: + logger.info( + f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " + "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." + ) + + # Set seed before initializing model. + set_seed(training_args.seed) + + # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) + # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ + # (the dataset will be downloaded automatically from the datasets Hub + # + # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this + # behavior (see below) + # + # In distributed training, the load_dataset function guarantee that only one local process can concurrently + # download the dataset. + if data_args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + raw_datasets = load_dataset( + data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir + ) + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + else: + data_files = {} + if data_args.train_file is not None: + data_files["train"] = data_args.train_file + extension = data_args.train_file.split(".")[-1] + if data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.validation_file.split(".")[-1] + if extension == "txt": + extension = "text" + raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, sep='\t') + + # If no validation data is there, validation_split_percentage will be used to divide the dataset. + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + extension, + data_files=data_files, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + extension, + data_files=data_files, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + + # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at + # https://huggingface.co/docs/datasets/loading_datasets.html. + + # Load pretrained model and tokenizer + # + # Distributed training: + # The .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + config_kwargs = { + "cache_dir": model_args.cache_dir, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.config_name: + config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) + elif model_args.model_name_or_path: + config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) + else: + config = CONFIG_MAPPING[model_args.model_type]() + logger.warning("You are instantiating a new config instance from scratch.") + if model_args.config_overrides is not None: + logger.info(f"Overriding config: {model_args.config_overrides}") + config.update_from_string(model_args.config_overrides) + + tokenizer_kwargs = { + "cache_dir": model_args.cache_dir, + "use_fast": model_args.use_fast_tokenizer, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.tokenizer_name: + tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) + elif model_args.model_name_or_path: + tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) + else: + raise ValueError( + "You are instantiating a new tokenizer from scratch. This is not supported by this script." + "You can do it from another script, save it, and load it from here, using --tokenizer_name." + ) + + if model_args.model_name_or_path: + model = AutoModelForMaskedLM.from_pretrained( + model_args.model_name_or_path, + from_tf=bool(".ckpt" in model_args.model_name_or_path), + config=config, + cache_dir=model_args.cache_dir, + revision=model_args.model_revision, + use_auth_token=True if model_args.use_auth_token else None, + ) + else: + logger.info("Training new model from scratch") + model = AutoModelForMaskedLM.from_config(config) + + model.resize_token_embeddings(len(tokenizer)) + + # Preprocessing the datasets. + # First we tokenize all the texts. + if training_args.do_train: + column_names = raw_datasets["train"].column_names + else: + column_names = raw_datasets["validation"].column_names + text_column_name = "text" if "text" in column_names else column_names[0] + + if data_args.max_seq_length is None: + max_seq_length = tokenizer.model_max_length + if max_seq_length > 1024: + logger.warning( + f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " + "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." + ) + max_seq_length = 1024 + else: + if data_args.max_seq_length > tokenizer.model_max_length: + logger.warning( + f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" + f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." + ) + max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) + + if data_args.line_by_line: + # When using line_by_line, we just tokenize each nonempty line. + padding = "max_length" if data_args.pad_to_max_length else False + + def tokenize_function(examples): + # Remove empty lines + examples[text_column_name] = [ + line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() + ] + return tokenizer( + examples[text_column_name], + padding=padding, + truncation=True, + max_length=max_seq_length, + # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it + # receives the `special_tokens_mask`. + return_special_tokens_mask=True, + ) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=[text_column_name], + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on dataset line_by_line", + ) + else: + # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. + # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more + # efficient when it receives the `special_tokens_mask`. + def tokenize_function(examples): + return tokenizer(examples[text_column_name], return_special_tokens_mask=True) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on every text in dataset", + ) + + # Main data processing function that will concatenate all texts from our dataset and generate chunks of + # max_seq_length. + def group_texts(examples): + # Concatenate all texts. + concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} + total_length = len(concatenated_examples[list(examples.keys())[0]]) + # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can + # customize this part to your needs. + if total_length >= max_seq_length: + total_length = (total_length // max_seq_length) * max_seq_length + # Split by chunks of max_len. + result = { + k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] + for k, t in concatenated_examples.items() + } + return result + + # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a + # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value + # might be slower to preprocess. + # + # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: + # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map + + with training_args.main_process_first(desc="grouping texts together"): + tokenized_datasets = tokenized_datasets.map( + group_texts, + batched=True, + num_proc=data_args.preprocessing_num_workers, + load_from_cache_file=not data_args.overwrite_cache, + desc=f"Grouping texts in chunks of {max_seq_length}", + ) + + if training_args.do_train: + if "train" not in tokenized_datasets: + raise ValueError("--do_train requires a train dataset") + train_dataset = tokenized_datasets["train"] + if data_args.max_train_samples is not None: + train_dataset = train_dataset.select(range(data_args.max_train_samples)) + + if training_args.do_eval: + if "validation" not in tokenized_datasets: + raise ValueError("--do_eval requires a validation dataset") + eval_dataset = tokenized_datasets["validation"] + if data_args.max_eval_samples is not None: + eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) + + # Data collator + # This one will take care of randomly masking the tokens. + pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length + data_collator = DataCollatorForLanguageModeling( + tokenizer=tokenizer, + mlm_probability=data_args.mlm_probability, + pad_to_multiple_of=8 if pad_to_multiple_of_8 else None, + ) + + # Initialize our Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset if training_args.do_train else None, + eval_dataset=eval_dataset if training_args.do_eval else None, + tokenizer=tokenizer, + data_collator=data_collator, + ) + + # Training + if training_args.do_train: + checkpoint = None + if training_args.resume_from_checkpoint is not None: + checkpoint = training_args.resume_from_checkpoint + elif last_checkpoint is not None: + checkpoint = last_checkpoint + train_result = trainer.train(resume_from_checkpoint=checkpoint) + trainer.save_model() # Saves the tokenizer too for easy upload + metrics = train_result.metrics + + max_train_samples = ( + data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) + ) + metrics["train_samples"] = min(max_train_samples, len(train_dataset)) + + trainer.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + + # Evaluation + if training_args.do_eval: + logger.info("*** Evaluate ***") + + metrics = trainer.evaluate() + + max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) + metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) + try: + perplexity = math.exp(metrics["eval_loss"]) + except OverflowError: + perplexity = float("inf") + metrics["perplexity"] = perplexity + + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"} + if data_args.dataset_name is not None: + kwargs["dataset_tags"] = data_args.dataset_name + if data_args.dataset_config_name is not None: + kwargs["dataset_args"] = data_args.dataset_config_name + kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" + else: + kwargs["dataset"] = data_args.dataset_name + + if training_args.push_to_hub: + trainer.push_to_hub(**kwargs) + else: + trainer.create_model_card(**kwargs) + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main() diff --git a/roberta_with_year_from_scratch/00_create_datasets.py b/roberta_with_year_from_scratch/00_create_datasets.py new file mode 100644 index 0000000..075a8ab --- /dev/null +++ b/roberta_with_year_from_scratch/00_create_datasets.py @@ -0,0 +1,32 @@ +import datetime + +with open('../test-A/in.tsv','r') as f_in, open(f'./test-A_in.csv', 'w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont, date, text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + text = 'year: ' + year +' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' '+ text + f_hf.write(text +'\n') + + +for dataset in 'train', 'dev-0': + with open(f'../{dataset}/in.tsv') as f_in, open(f'./{dataset}_in.csv','w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont,date,text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + text = 'year: ' + year +' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' '+ text + f_hf.write(text +'\n') + diff --git a/roberta_with_year_from_scratch/04_predict.py b/roberta_with_year_from_scratch/04_predict.py new file mode 100644 index 0000000..1634df1 --- /dev/null +++ b/roberta_with_year_from_scratch/04_predict.py @@ -0,0 +1,25 @@ +from tqdm import tqdm +from transformers import pipeline + +def get_formatted(text): + answers = unmasker(text, top_k=15) + answers = {x['token_str']:x['score'] for x in answers} + empty = 1 - sum(answers.values()) + answers[''] = empty + answers_str ='' + for k,v in answers.items(): + answers_str += k.strip()+':'+str(v) + ' ' + return answers_str.rstrip(' ').lstrip(' ') + +def write(f_path_in, f_path_out): + with open(f_path_in) as f_in, open(f_path_out,'w') as f_out: + next(f_in) + for line in tqdm(f_in,total=150_000): + l_in = line.rstrip() + a = get_formatted(l_in) + f_out.write(a + '\n') + +model = 'robertamodel' +unmasker = pipeline('fill-mask', model=model, device=0) +write('dev-0_in.csv', '../dev-0/out.tsv') +write('test-A_in.csv', '../test-A/out.tsv') diff --git a/roberta_with_year_from_scratch/1_train_tokenizer.py b/roberta_with_year_from_scratch/1_train_tokenizer.py new file mode 100644 index 0000000..5b8c980 --- /dev/null +++ b/roberta_with_year_from_scratch/1_train_tokenizer.py @@ -0,0 +1,18 @@ +from pathlib import Path +from tokenizers import ByteLevelBPETokenizer + +paths = ['./train_in.csv'] + +# Initialize a tokenizer +tokenizer = ByteLevelBPETokenizer() + +# Customize training +tokenizer.train(files=paths, vocab_size=50265, min_frequency=2, special_tokens=[ + "", + "", + "", + "", + "", +]) + +tokenizer.save_model("./tokenizer_model") diff --git a/roberta_with_year_from_scratch/2_run.sh b/roberta_with_year_from_scratch/2_run.sh new file mode 100644 index 0000000..cde527f --- /dev/null +++ b/roberta_with_year_from_scratch/2_run.sh @@ -0,0 +1,22 @@ +#if [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then echo "CUDA_VISIBLE_DEVICES NOT SET"; exit 1 ; else echo "using cuda devices '$CUDA_VISIBLE_DEVICES'"; fi + +python run_mlm.py \ + --model_name_or_path roberta-base \ + --max_seq_length 64 \ + --output_dir ./robertamodel \ + --train_file ./train_in.csv \ + --validation_file ./dev-0_in.csv \ + --do_train \ + --do_eval \ + --per_device_train_batch_size=256 \ + --per_device_eval_batch_size=256 \ + --gradient_accumulation_steps=1 \ + --fp16 False \ + --save_steps 1000 \ + --eval_steps 1000 \ + --logging_steps 1000 \ + --evaluation_strategy steps \ + --num_train_epochs 40 \ + --warmup_steps 1000 \ + --tokenizer_name tokenizer_model \ + | tee --append logs_regular.txt diff --git a/roberta_with_year_from_scratch/run.sh b/roberta_with_year_from_scratch/run.sh new file mode 100644 index 0000000..c1cc19d --- /dev/null +++ b/roberta_with_year_from_scratch/run.sh @@ -0,0 +1,4 @@ +python 00_create_datasets.py +python 1_train_tokenizer.py +bash 2_run.sh +python 04_predict.py diff --git a/roberta_with_year_from_scratch/run_mlm.py b/roberta_with_year_from_scratch/run_mlm.py new file mode 100755 index 0000000..e3e89dc --- /dev/null +++ b/roberta_with_year_from_scratch/run_mlm.py @@ -0,0 +1,556 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2020 The HuggingFace Team All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset. + +Here is the full list of checkpoints on the hub that can be fine-tuned by this script: +https://huggingface.co/models?filter=masked-lm +""" +# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. + +import logging +import math +import os +import sys +from dataclasses import dataclass, field +from typing import Optional + +import datasets +from datasets import load_dataset + +import transformers +from transformers import ( + CONFIG_MAPPING, + MODEL_FOR_MASKED_LM_MAPPING, + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + HfArgumentParser, + Trainer, + TrainingArguments, + set_seed, +) +from transformers.trainer_utils import get_last_checkpoint +from transformers.utils import check_min_version +from transformers.utils.versions import require_version + + +# Will error if the minimal version of Transformers is not installed. Remove at your own risks. +check_min_version("4.12.0.dev0") + +require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") + +logger = logging.getLogger(__name__) +MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) +MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) + + +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. + """ + + model_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The model checkpoint for weights initialization." + "Don't set if you want to train a model from scratch." + }, + ) + model_type: Optional[str] = field( + default=None, + metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, + ) + config_overrides: Optional[str] = field( + default=None, + metadata={ + "help": "Override some existing default config settings when a model is trained from scratch. Example: " + "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" + }, + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + model_revision: str = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, + ) + use_auth_token: bool = field( + default=False, + metadata={ + "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script " + "with private models)." + }, + ) + + def __post_init__(self): + if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): + raise ValueError( + "--config_overrides can't be used in combination with --config_name or --model_name_or_path" + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + dataset_name: Optional[str] = field( + default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: Optional[str] = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) + validation_file: Optional[str] = field( + default=None, + metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + validation_split_percentage: Optional[int] = field( + default=5, + metadata={ + "help": "The percentage of the train set used as validation set in case there's no validation split" + }, + ) + max_seq_length: Optional[int] = field( + default=None, + metadata={ + "help": "The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated." + }, + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + mlm_probability: float = field( + default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} + ) + line_by_line: bool = field( + default=False, + metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, + ) + pad_to_max_length: bool = field( + default=False, + metadata={ + "help": "Whether to pad all samples to `max_seq_length`. " + "If False, will pad the samples dynamically when batching to the maximum length in the batch." + }, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + }, + ) + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." + + +def main(): + # See all possible arguments in src/transformers/training_args.py + # or by passing the --help flag to this script. + # We now keep distinct sets of args, for a cleaner separation of concerns. + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + # Setup logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + + log_level = training_args.get_process_log_level() + logger.setLevel(log_level) + datasets.utils.logging.set_verbosity(log_level) + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_default_handler() + transformers.utils.logging.enable_explicit_format() + + # Log on each process the small summary: + logger.warning( + f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + # Set the verbosity to info of the Transformers logger (on main process only): + logger.info(f"Training/evaluation parameters {training_args}") + + # Detecting last checkpoint. + last_checkpoint = None + if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: + last_checkpoint = get_last_checkpoint(training_args.output_dir) + if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: + raise ValueError( + f"Output directory ({training_args.output_dir}) already exists and is not empty. " + "Use --overwrite_output_dir to overcome." + ) + elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: + logger.info( + f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " + "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." + ) + + # Set seed before initializing model. + set_seed(training_args.seed) + + # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) + # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ + # (the dataset will be downloaded automatically from the datasets Hub + # + # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this + # behavior (see below) + # + # In distributed training, the load_dataset function guarantee that only one local process can concurrently + # download the dataset. + if data_args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + raw_datasets = load_dataset( + data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir + ) + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + else: + data_files = {} + if data_args.train_file is not None: + data_files["train"] = data_args.train_file + extension = data_args.train_file.split(".")[-1] + if data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.validation_file.split(".")[-1] + if extension == "txt": + extension = "text" + raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, sep='\t') + + # If no validation data is there, validation_split_percentage will be used to divide the dataset. + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + extension, + data_files=data_files, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + extension, + data_files=data_files, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + + # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at + # https://huggingface.co/docs/datasets/loading_datasets.html. + + # Load pretrained model and tokenizer + # + # Distributed training: + # The .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + config_kwargs = { + "cache_dir": model_args.cache_dir, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.config_name: + config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) + elif model_args.model_name_or_path: + config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) + else: + config = CONFIG_MAPPING[model_args.model_type]() + logger.warning("You are instantiating a new config instance from scratch.") + if model_args.config_overrides is not None: + logger.info(f"Overriding config: {model_args.config_overrides}") + config.update_from_string(model_args.config_overrides) + + tokenizer_kwargs = { + "cache_dir": model_args.cache_dir, + "use_fast": model_args.use_fast_tokenizer, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.tokenizer_name: + tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) + elif model_args.model_name_or_path: + tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) + else: + raise ValueError( + "You are instantiating a new tokenizer from scratch. This is not supported by this script." + "You can do it from another script, save it, and load it from here, using --tokenizer_name." + ) + + if model_args.model_name_or_path: + model = AutoModelForMaskedLM.from_pretrained( + model_args.model_name_or_path, + from_tf=bool(".ckpt" in model_args.model_name_or_path), + config=config, + cache_dir=model_args.cache_dir, + revision=model_args.model_revision, + use_auth_token=True if model_args.use_auth_token else None, + ) + from transformers import RobertaTokenizer, RobertaForMaskedLM, RobertaConfig + config = model.config + model = RobertaForMaskedLM(config) + + else: + logger.info("Training new model from scratch") + model = AutoModelForMaskedLM.from_config(config) + + model.resize_token_embeddings(len(tokenizer)) + + # Preprocessing the datasets. + # First we tokenize all the texts. + if training_args.do_train: + column_names = raw_datasets["train"].column_names + else: + column_names = raw_datasets["validation"].column_names + text_column_name = "text" if "text" in column_names else column_names[0] + + if data_args.max_seq_length is None: + max_seq_length = tokenizer.model_max_length + if max_seq_length > 1024: + logger.warning( + f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " + "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." + ) + max_seq_length = 1024 + else: + if data_args.max_seq_length > tokenizer.model_max_length: + logger.warning( + f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" + f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." + ) + max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) + + if data_args.line_by_line: + # When using line_by_line, we just tokenize each nonempty line. + padding = "max_length" if data_args.pad_to_max_length else False + + def tokenize_function(examples): + # Remove empty lines + examples[text_column_name] = [ + line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() + ] + return tokenizer( + examples[text_column_name], + padding=padding, + truncation=True, + max_length=max_seq_length, + # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it + # receives the `special_tokens_mask`. + return_special_tokens_mask=True, + ) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=[text_column_name], + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on dataset line_by_line", + ) + else: + # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. + # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more + # efficient when it receives the `special_tokens_mask`. + def tokenize_function(examples): + return tokenizer(examples[text_column_name], return_special_tokens_mask=True) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on every text in dataset", + ) + + # Main data processing function that will concatenate all texts from our dataset and generate chunks of + # max_seq_length. + def group_texts(examples): + # Concatenate all texts. + concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} + total_length = len(concatenated_examples[list(examples.keys())[0]]) + # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can + # customize this part to your needs. + if total_length >= max_seq_length: + total_length = (total_length // max_seq_length) * max_seq_length + # Split by chunks of max_len. + result = { + k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] + for k, t in concatenated_examples.items() + } + return result + + # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a + # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value + # might be slower to preprocess. + # + # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: + # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map + + with training_args.main_process_first(desc="grouping texts together"): + tokenized_datasets = tokenized_datasets.map( + group_texts, + batched=True, + num_proc=data_args.preprocessing_num_workers, + load_from_cache_file=not data_args.overwrite_cache, + desc=f"Grouping texts in chunks of {max_seq_length}", + ) + + if training_args.do_train: + if "train" not in tokenized_datasets: + raise ValueError("--do_train requires a train dataset") + train_dataset = tokenized_datasets["train"] + if data_args.max_train_samples is not None: + train_dataset = train_dataset.select(range(data_args.max_train_samples)) + + if training_args.do_eval: + if "validation" not in tokenized_datasets: + raise ValueError("--do_eval requires a validation dataset") + eval_dataset = tokenized_datasets["validation"] + if data_args.max_eval_samples is not None: + eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) + + # Data collator + # This one will take care of randomly masking the tokens. + pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length + data_collator = DataCollatorForLanguageModeling( + tokenizer=tokenizer, + mlm_probability=data_args.mlm_probability, + pad_to_multiple_of=8 if pad_to_multiple_of_8 else None, + ) + + # Initialize our Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset if training_args.do_train else None, + eval_dataset=eval_dataset if training_args.do_eval else None, + tokenizer=tokenizer, + data_collator=data_collator, + ) + + # Training + if training_args.do_train: + checkpoint = None + if training_args.resume_from_checkpoint is not None: + checkpoint = training_args.resume_from_checkpoint + elif last_checkpoint is not None: + checkpoint = last_checkpoint + train_result = trainer.train(resume_from_checkpoint=checkpoint) + trainer.save_model() # Saves the tokenizer too for easy upload + metrics = train_result.metrics + + max_train_samples = ( + data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) + ) + metrics["train_samples"] = min(max_train_samples, len(train_dataset)) + + trainer.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + + # Evaluation + if training_args.do_eval: + logger.info("*** Evaluate ***") + + metrics = trainer.evaluate() + + max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) + metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) + try: + perplexity = math.exp(metrics["eval_loss"]) + except OverflowError: + perplexity = float("inf") + metrics["perplexity"] = perplexity + + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"} + if data_args.dataset_name is not None: + kwargs["dataset_tags"] = data_args.dataset_name + if data_args.dataset_config_name is not None: + kwargs["dataset_args"] = data_args.dataset_config_name + kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" + else: + kwargs["dataset"] = data_args.dataset_name + + if training_args.push_to_hub: + trainer.push_to_hub(**kwargs) + else: + trainer.create_model_card(**kwargs) + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main() diff --git a/roberta_with_year_from_scratch/tokenizer_config.json b/roberta_with_year_from_scratch/tokenizer_config.json new file mode 100644 index 0000000..cbc6ef8 --- /dev/null +++ b/roberta_with_year_from_scratch/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": "", "bos_token": "", "eos_token": "", "add_prefix_space": false, "errors": "replace", "sep_token": "", "cls_token": "", "pad_token": "", "mask_token": "", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"} \ No newline at end of file diff --git a/roberta_with_year_from_scratch/tokenizer_model/.gitkeep b/roberta_with_year_from_scratch/tokenizer_model/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/roberta_with_year_from_scratch/tokenizer_model/tokenizer_config.json b/roberta_with_year_from_scratch/tokenizer_model/tokenizer_config.json new file mode 100644 index 0000000..cbc6ef8 --- /dev/null +++ b/roberta_with_year_from_scratch/tokenizer_model/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": "", "bos_token": "", "eos_token": "", "add_prefix_space": false, "errors": "replace", "sep_token": "", "cls_token": "", "pad_token": "", "mask_token": "", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"} \ No newline at end of file diff --git a/roberta_with_year_ft/00_create_datasets.py b/roberta_with_year_ft/00_create_datasets.py new file mode 100644 index 0000000..075a8ab --- /dev/null +++ b/roberta_with_year_ft/00_create_datasets.py @@ -0,0 +1,32 @@ +import datetime + +with open('../test-A/in.tsv','r') as f_in, open(f'./test-A_in.csv', 'w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont, date, text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + text = 'year: ' + year +' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' '+ text + f_hf.write(text +'\n') + + +for dataset in 'train', 'dev-0': + with open(f'../{dataset}/in.tsv') as f_in, open(f'./{dataset}_in.csv','w') as f_hf: + f_hf.write('text\n') + for line_in in f_in: + year_cont,date,text = line_in.rstrip('\n').split('\t') + d = datetime.datetime.strptime(date,"%Y%m%d") + day_of_year = str(d.timetuple().tm_yday) + day_of_month = str(d.day) + month = str(d.month) + year = str(d.year) + weekday = str(d.weekday()) + day_of_year = str(d.timetuple().tm_yday) + text = 'year: ' + year +' month: ' + month + ' day: ' + day_of_month + ' weekday: ' + weekday + ' '+ text + f_hf.write(text +'\n') + diff --git a/roberta_with_year_ft/04_predict.py b/roberta_with_year_ft/04_predict.py new file mode 100644 index 0000000..1634df1 --- /dev/null +++ b/roberta_with_year_ft/04_predict.py @@ -0,0 +1,25 @@ +from tqdm import tqdm +from transformers import pipeline + +def get_formatted(text): + answers = unmasker(text, top_k=15) + answers = {x['token_str']:x['score'] for x in answers} + empty = 1 - sum(answers.values()) + answers[''] = empty + answers_str ='' + for k,v in answers.items(): + answers_str += k.strip()+':'+str(v) + ' ' + return answers_str.rstrip(' ').lstrip(' ') + +def write(f_path_in, f_path_out): + with open(f_path_in) as f_in, open(f_path_out,'w') as f_out: + next(f_in) + for line in tqdm(f_in,total=150_000): + l_in = line.rstrip() + a = get_formatted(l_in) + f_out.write(a + '\n') + +model = 'robertamodel' +unmasker = pipeline('fill-mask', model=model, device=0) +write('dev-0_in.csv', '../dev-0/out.tsv') +write('test-A_in.csv', '../test-A/out.tsv') diff --git a/roberta_with_year_ft/2_run.sh b/roberta_with_year_ft/2_run.sh new file mode 100644 index 0000000..bb9c281 --- /dev/null +++ b/roberta_with_year_ft/2_run.sh @@ -0,0 +1,21 @@ +#if [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then echo "CUDA_VISIBLE_DEVICES NOT SET"; exit 1 ; else echo "using cuda devices '$CUDA_VISIBLE_DEVICES'"; fi + +python run_mlm.py \ + --model_name_or_path roberta-base \ + --max_seq_length 64 \ + --output_dir ./robertamodel \ + --train_file ./train_in.csv \ + --validation_file ./dev-0_in.csv \ + --do_train \ + --do_eval \ + --per_device_train_batch_size=256 \ + --per_device_eval_batch_size=256 \ + --gradient_accumulation_steps=1 \ + --fp16 False \ + --save_steps 1000 \ + --eval_steps 1000 \ + --logging_steps 1000 \ + --evaluation_strategy steps \ + --num_train_epochs 40 \ + --warmup_steps 1000 \ + | tee --append logs_regular.txt diff --git a/roberta_with_year_ft/run.sh b/roberta_with_year_ft/run.sh new file mode 100644 index 0000000..a35e620 --- /dev/null +++ b/roberta_with_year_ft/run.sh @@ -0,0 +1,3 @@ +python 00_create_datasets.py +bash 2_run.sh +python 04_predict.py diff --git a/roberta_with_year_ft/run_mlm.py b/roberta_with_year_ft/run_mlm.py new file mode 100755 index 0000000..306a3ce --- /dev/null +++ b/roberta_with_year_ft/run_mlm.py @@ -0,0 +1,552 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2020 The HuggingFace Team All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset. + +Here is the full list of checkpoints on the hub that can be fine-tuned by this script: +https://huggingface.co/models?filter=masked-lm +""" +# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. + +import logging +import math +import os +import sys +from dataclasses import dataclass, field +from typing import Optional + +import datasets +from datasets import load_dataset + +import transformers +from transformers import ( + CONFIG_MAPPING, + MODEL_FOR_MASKED_LM_MAPPING, + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + HfArgumentParser, + Trainer, + TrainingArguments, + set_seed, +) +from transformers.trainer_utils import get_last_checkpoint +from transformers.utils import check_min_version +from transformers.utils.versions import require_version + + +# Will error if the minimal version of Transformers is not installed. Remove at your own risks. +check_min_version("4.12.0.dev0") + +require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") + +logger = logging.getLogger(__name__) +MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) +MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) + + +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. + """ + + model_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The model checkpoint for weights initialization." + "Don't set if you want to train a model from scratch." + }, + ) + model_type: Optional[str] = field( + default=None, + metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, + ) + config_overrides: Optional[str] = field( + default=None, + metadata={ + "help": "Override some existing default config settings when a model is trained from scratch. Example: " + "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" + }, + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + model_revision: str = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, + ) + use_auth_token: bool = field( + default=False, + metadata={ + "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script " + "with private models)." + }, + ) + + def __post_init__(self): + if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): + raise ValueError( + "--config_overrides can't be used in combination with --config_name or --model_name_or_path" + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + dataset_name: Optional[str] = field( + default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: Optional[str] = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) + validation_file: Optional[str] = field( + default=None, + metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + validation_split_percentage: Optional[int] = field( + default=5, + metadata={ + "help": "The percentage of the train set used as validation set in case there's no validation split" + }, + ) + max_seq_length: Optional[int] = field( + default=None, + metadata={ + "help": "The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated." + }, + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + mlm_probability: float = field( + default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} + ) + line_by_line: bool = field( + default=False, + metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, + ) + pad_to_max_length: bool = field( + default=False, + metadata={ + "help": "Whether to pad all samples to `max_seq_length`. " + "If False, will pad the samples dynamically when batching to the maximum length in the batch." + }, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + }, + ) + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." + + +def main(): + # See all possible arguments in src/transformers/training_args.py + # or by passing the --help flag to this script. + # We now keep distinct sets of args, for a cleaner separation of concerns. + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + # Setup logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + + log_level = training_args.get_process_log_level() + logger.setLevel(log_level) + datasets.utils.logging.set_verbosity(log_level) + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_default_handler() + transformers.utils.logging.enable_explicit_format() + + # Log on each process the small summary: + logger.warning( + f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + # Set the verbosity to info of the Transformers logger (on main process only): + logger.info(f"Training/evaluation parameters {training_args}") + + # Detecting last checkpoint. + last_checkpoint = None + if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: + last_checkpoint = get_last_checkpoint(training_args.output_dir) + if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: + raise ValueError( + f"Output directory ({training_args.output_dir}) already exists and is not empty. " + "Use --overwrite_output_dir to overcome." + ) + elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: + logger.info( + f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " + "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." + ) + + # Set seed before initializing model. + set_seed(training_args.seed) + + # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) + # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ + # (the dataset will be downloaded automatically from the datasets Hub + # + # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this + # behavior (see below) + # + # In distributed training, the load_dataset function guarantee that only one local process can concurrently + # download the dataset. + if data_args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + raw_datasets = load_dataset( + data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir + ) + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + else: + data_files = {} + if data_args.train_file is not None: + data_files["train"] = data_args.train_file + extension = data_args.train_file.split(".")[-1] + if data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.validation_file.split(".")[-1] + if extension == "txt": + extension = "text" + raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir, sep='\t') + + # If no validation data is there, validation_split_percentage will be used to divide the dataset. + if "validation" not in raw_datasets.keys(): + raw_datasets["validation"] = load_dataset( + extension, + data_files=data_files, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + raw_datasets["train"] = load_dataset( + extension, + data_files=data_files, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + + # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at + # https://huggingface.co/docs/datasets/loading_datasets.html. + + # Load pretrained model and tokenizer + # + # Distributed training: + # The .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + config_kwargs = { + "cache_dir": model_args.cache_dir, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.config_name: + config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) + elif model_args.model_name_or_path: + config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) + else: + config = CONFIG_MAPPING[model_args.model_type]() + logger.warning("You are instantiating a new config instance from scratch.") + if model_args.config_overrides is not None: + logger.info(f"Overriding config: {model_args.config_overrides}") + config.update_from_string(model_args.config_overrides) + + tokenizer_kwargs = { + "cache_dir": model_args.cache_dir, + "use_fast": model_args.use_fast_tokenizer, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + if model_args.tokenizer_name: + tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) + elif model_args.model_name_or_path: + tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) + else: + raise ValueError( + "You are instantiating a new tokenizer from scratch. This is not supported by this script." + "You can do it from another script, save it, and load it from here, using --tokenizer_name." + ) + + if model_args.model_name_or_path: + model = AutoModelForMaskedLM.from_pretrained( + model_args.model_name_or_path, + from_tf=bool(".ckpt" in model_args.model_name_or_path), + config=config, + cache_dir=model_args.cache_dir, + revision=model_args.model_revision, + use_auth_token=True if model_args.use_auth_token else None, + ) + else: + logger.info("Training new model from scratch") + model = AutoModelForMaskedLM.from_config(config) + + model.resize_token_embeddings(len(tokenizer)) + + # Preprocessing the datasets. + # First we tokenize all the texts. + if training_args.do_train: + column_names = raw_datasets["train"].column_names + else: + column_names = raw_datasets["validation"].column_names + text_column_name = "text" if "text" in column_names else column_names[0] + + if data_args.max_seq_length is None: + max_seq_length = tokenizer.model_max_length + if max_seq_length > 1024: + logger.warning( + f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " + "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx." + ) + max_seq_length = 1024 + else: + if data_args.max_seq_length > tokenizer.model_max_length: + logger.warning( + f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" + f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." + ) + max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) + + if data_args.line_by_line: + # When using line_by_line, we just tokenize each nonempty line. + padding = "max_length" if data_args.pad_to_max_length else False + + def tokenize_function(examples): + # Remove empty lines + examples[text_column_name] = [ + line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() + ] + return tokenizer( + examples[text_column_name], + padding=padding, + truncation=True, + max_length=max_seq_length, + # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it + # receives the `special_tokens_mask`. + return_special_tokens_mask=True, + ) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=[text_column_name], + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on dataset line_by_line", + ) + else: + # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. + # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more + # efficient when it receives the `special_tokens_mask`. + def tokenize_function(examples): + return tokenizer(examples[text_column_name], return_special_tokens_mask=True) + + with training_args.main_process_first(desc="dataset map tokenization"): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on every text in dataset", + ) + + # Main data processing function that will concatenate all texts from our dataset and generate chunks of + # max_seq_length. + def group_texts(examples): + # Concatenate all texts. + concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} + total_length = len(concatenated_examples[list(examples.keys())[0]]) + # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can + # customize this part to your needs. + if total_length >= max_seq_length: + total_length = (total_length // max_seq_length) * max_seq_length + # Split by chunks of max_len. + result = { + k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] + for k, t in concatenated_examples.items() + } + return result + + # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a + # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value + # might be slower to preprocess. + # + # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: + # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map + + with training_args.main_process_first(desc="grouping texts together"): + tokenized_datasets = tokenized_datasets.map( + group_texts, + batched=True, + num_proc=data_args.preprocessing_num_workers, + load_from_cache_file=not data_args.overwrite_cache, + desc=f"Grouping texts in chunks of {max_seq_length}", + ) + + if training_args.do_train: + if "train" not in tokenized_datasets: + raise ValueError("--do_train requires a train dataset") + train_dataset = tokenized_datasets["train"] + if data_args.max_train_samples is not None: + train_dataset = train_dataset.select(range(data_args.max_train_samples)) + + if training_args.do_eval: + if "validation" not in tokenized_datasets: + raise ValueError("--do_eval requires a validation dataset") + eval_dataset = tokenized_datasets["validation"] + if data_args.max_eval_samples is not None: + eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) + + # Data collator + # This one will take care of randomly masking the tokens. + pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length + data_collator = DataCollatorForLanguageModeling( + tokenizer=tokenizer, + mlm_probability=data_args.mlm_probability, + pad_to_multiple_of=8 if pad_to_multiple_of_8 else None, + ) + + # Initialize our Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset if training_args.do_train else None, + eval_dataset=eval_dataset if training_args.do_eval else None, + tokenizer=tokenizer, + data_collator=data_collator, + ) + + # Training + if training_args.do_train: + checkpoint = None + if training_args.resume_from_checkpoint is not None: + checkpoint = training_args.resume_from_checkpoint + elif last_checkpoint is not None: + checkpoint = last_checkpoint + train_result = trainer.train(resume_from_checkpoint=checkpoint) + trainer.save_model() # Saves the tokenizer too for easy upload + metrics = train_result.metrics + + max_train_samples = ( + data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) + ) + metrics["train_samples"] = min(max_train_samples, len(train_dataset)) + + trainer.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + + # Evaluation + if training_args.do_eval: + logger.info("*** Evaluate ***") + + metrics = trainer.evaluate() + + max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) + metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) + try: + perplexity = math.exp(metrics["eval_loss"]) + except OverflowError: + perplexity = float("inf") + metrics["perplexity"] = perplexity + + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"} + if data_args.dataset_name is not None: + kwargs["dataset_tags"] = data_args.dataset_name + if data_args.dataset_config_name is not None: + kwargs["dataset_args"] = data_args.dataset_config_name + kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" + else: + kwargs["dataset"] = data_args.dataset_name + + if training_args.push_to_hub: + trainer.push_to_hub(**kwargs) + else: + trainer.create_model_card(**kwargs) + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main()