#!/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 functools import logging import math import os import sys from dataclasses import dataclass, field from typing import Optional import datasets import transformers from datasets import load_dataset, load_from_disk from torch.optim.lr_scheduler import LambdaLR from transformers import ( CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForPreTraining, AutoTokenizer, HfArgumentParser, set_seed, ) from transformers.data import default_data_collator from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version from transformers.utils.versions import require_version from optimum.graphcore import IPUConfig, IPUTrainer from optimum.graphcore import IPUTrainingArguments as TrainingArguments from optimum.graphcore.data import DataCollatorForLanguageModelingWithMaxTokensMasked, pad_on_batch_axis from optimum.graphcore.utils import gc_check_min_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.29.0") # Will error if the minimal version of Optimum Graphcore is not installed. Remove at your own risks. gc_check_min_version("0.6.0.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) def get_polynomial_decay_schedule_with_polynomial_warmup( optimizer, warmup_ratio, num_training_steps, lr_end=1e-7, power=1.0, warmup_power=1.0, last_epoch=-1 ): """ Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Here, warmup is also behaving as a polynomial function. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. warmup_ratio (`float`): Percentage of steps to be dedicated to warmup phase. num_training_steps (`int`): The total number of training steps. lr_end (`float`, *optional*, defaults to 1e-7): The end LR. power (`float`, *optional*, defaults to 1.0): Power factor. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT implementation at https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37 Return: `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ num_warmup_steps = math.ceil(num_training_steps * warmup_ratio) lr_init = optimizer.defaults["lr"] if not (lr_init > lr_end): raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})") def lr_lambda(current_step: int): if current_step < num_warmup_steps: return (current_step**warmup_power) / (num_warmup_steps**warmup_power) elif current_step > num_training_steps: return lr_end / lr_init # as LambdaLR multiplies by lr_init else: lr_range = lr_init - lr_end decay_steps = num_training_steps - num_warmup_steps pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps decay = lr_range * pct_remaining**power + lr_end return decay / lr_init # as LambdaLR multiplies by lr_init return LambdaLR(optimizer, lr_lambda, last_epoch) @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 `huggingface-cli login` (necessary to use this script " "with private models)." }, ) groupbert_schedule: bool = field( default=False, metadata={ "help": "Use to prevent using a prebuilt lr schedule that uses a polynomial warmup and pass it to the Trainer.)." }, ) # 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)."} ) dataset_path: Optional[str] = field( default=None, metadata={ "help": "The path to the dataset that was saved using Dataset.save_to_disk(), the dataset will be loaded using datasets.load_from_disk." }, ) 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)."}, ) data_dir: Optional[str] = field( default=None, metadata={"help": "The path to a local folder containing the data files."} ) 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" }, ) is_already_preprocessed: Optional[bool] = field( default=False, metadata={ "help": "Whether the dataset is already pre-processed or not. If True, all the pre-processing steps will be" "skipped." }, ) is_already_masked: Optional[bool] = field( default=False, metadata={"help": "Whether the input ids are already (statically) masked in the dataset."}, ) 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.dataset_path 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] if extension not in ["csv", "json", "txt"]: raise ValueError("`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] if extension not in ["csv", "json", "txt"]: raise ValueError("`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, data_dir=data_args.data_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, data_dir=data_args.data_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, data_dir=data_args.data_dir, ) elif data_args.dataset_path is not None: raw_datasets = load_from_disk(data_args.dataset_path) # TODO: define behaviour when no validation split is provided. # if "validation" not in raw_datasets.keys(): # raw_datasets["validation"] = datasets.Dataset() 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) # 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) if training_args.ipu_config_name: ipu_config = IPUConfig.from_pretrained(training_args.ipu_config_name, **config_kwargs) elif model_args.model_name_or_path: ipu_config = IPUConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) else: raise RuntimeError("You must provide an IPUConfig") 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 = AutoModelForPreTraining.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 = AutoModelForPreTraining.from_config(config) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) > embedding_size: 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.is_already_preprocessed: logger.warning( "The dataset is already pre-processed, skipping pre-processing. Make sure that you are using the same tokenizer as the ones used during the dataset creation." ) tokenized_datasets = raw_datasets else: 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)) if not data_args.is_already_masked: # 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 = DataCollatorForLanguageModelingWithMaxTokensMasked( max_seq_length=len(next(iter(train_dataset))["labels"]) if data_args.is_already_preprocessed else max_seq_length, tokenizer=tokenizer, mlm_probability=data_args.mlm_probability, # pad_to_multiple_of=8 if pad_to_multiple_of_8 else None, pad_to_multiple_of=None, ) model.config.max_num_masked_tokens = data_collator.max_num_masked_tokens else: data_collator = default_data_collator if training_args.do_train and training_args.pad_on_batch_axis: logger.info( "Padding on batch axis enabled, each batch feeded to the compiled model during training will have the proper size" ) data_collator_wrapper = pad_on_batch_axis( training_args.per_device_train_batch_size * ipu_config.batch_size_factor(), {k: -100 if k in ["labels", "next_sentence_label"] else 0 for k in train_dataset.column_names}, ) data_collator = data_collator_wrapper(data_collator) optimizer, lr_schedule = (None, None) if model_args.groupbert_schedule: lr_schedule = functools.partial( get_polynomial_decay_schedule_with_polynomial_warmup, warmup_ratio=training_args.warmup_ratio, num_training_steps=training_args.max_steps, warmup_power=0.5, ) # Initialize our Trainer trainer = IPUTrainer( model=model, ipu_config=ipu_config, 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, optimizers=(optimizer, lr_schedule), ) # 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()