#!/usr/bin/env python # coding=utf-8 # Copyright 2020 The HuggingFace Inc. 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 causal language modeling (GPT, GPT-2, CTRL, ...) 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=text-generation """ # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. import logging import math import os # disable logging until training starts os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import sys from dataclasses import dataclass, field from itertools import chain from typing import Optional import datasets import evaluate import torch from datasets import load_dataset import transformers from transformers import ( CONFIG_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, is_torch_tpu_available, set_seed, ) from transformers.testing_utils import CaptureLogger from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_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="gpt2", 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="gpt2", metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default="gpt2", 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)." ) }, ) torch_dtype: Optional[str] = field( default=None, metadata={ "help": ( "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the " "dtype will be automatically derived from the model's weights." ), "choices": ["auto", "bfloat16", "float16", "float32"], }, ) low_cpu_mem_usage: bool = field( default=False, metadata={ "help": ( "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded." "set True will benefit LLM loading time and RAM consumption." ) }, ) 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="babyLM_for_hf.py", metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default="babyLM-10M", 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)."}, ) 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=200, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"}) block_size: Optional[int] = field( default=None, metadata={ "help": ( "Optional input sequence length after tokenization. " "The training dataset will be truncated in block of this size for training. " "Default to the model max input length for single sentence inputs (take into account special tokens)." ) }, ) 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" }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) keep_linebreaks: bool = field( default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."} ) def __post_init__(self): if self.streaming: require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`") 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: if "--output_dir" not in sys.argv: sys.argv.append("--output_dir") sys.argv.append("./output") model_args, data_args, training_args = parser.parse_args_into_dataclasses() # by default we do both training and evaluation training_args.do_train = True if not "--do_train" in sys.argv else training_args.do_train training_args.do_eval = True if not "--do_eval" in sys.argv else training_args.do_eval training_args.overwrite_output_dir = True if not "--overwrite_output_dir" in sys.argv else training_args.overwrite_output_dir training_args.report_to = [] if not "--report_to" in sys.argv else training_args.report_to training_args.log_level = "critical" if not "--log_level" in sys.argv else training_args.log_level training_args.num_train_epochs = 1 if not "--num_train_epochs" in sys.argv else training_args.num_train_epochs training_args.evaluation_strategy = "steps" if not "--evaluation_strategy" in sys.argv else training_args.evaluation_strategy training_args.eval_steps = 0.2 if not "--eval_steps" in sys.argv else training_args.eval_steps training_args.per_device_train_batch_size = 16 if not "--per_device_train_batch_size" in sys.argv else training_args.per_device_train_batch_size # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_clm", model_args, data_args) # 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)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() 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}" ) 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 if no column called # 'text' is found. 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, use_auth_token=True if model_args.use_auth_token else None, streaming=data_args.streaming, ) 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, use_auth_token=True if model_args.use_auth_token else None, streaming=data_args.streaming, ) 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, use_auth_token=True if model_args.use_auth_token else None, streaming=data_args.streaming, ) else: data_files = {} dataset_args = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = ( data_args.train_file.split(".")[-1] if data_args.train_file is not None else data_args.validation_file.split(".")[-1] ) if extension == "txt": extension = "text" dataset_args["keep_linebreaks"] = data_args.keep_linebreaks raw_datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) # 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, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) raw_datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, **dataset_args, ) # 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) logger.info(f"New config: {config}") 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: torch_dtype = ( model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype) ) model = AutoModelForCausalLM.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, torch_dtype=torch_dtype, low_cpu_mem_usage=model_args.low_cpu_mem_usage, ) else: model = AutoModelForCausalLM.from_config(config) n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values()) logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params") # 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 = list(raw_datasets["train"].features) else: column_names = list(raw_datasets["validation"].features) text_column_name = "text" if "text" in column_names else column_names[0] # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base") def tokenize_function(examples): with CaptureLogger(tok_logger) as cl: output = tokenizer(examples[text_column_name]) # clm input could be much much longer than block_size if "Token indices sequence length is longer than the" in cl.out: tok_logger.warning( "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits" " before being passed to the model." ) return output with training_args.main_process_first(desc="dataset map tokenization"): if not data_args.streaming: 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 dataset", ) else: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, ) if data_args.block_size is None: block_size = tokenizer.model_max_length if block_size > 1024: logger.warning( "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value" " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can" " override this default with `--block_size xxx`." ) block_size = 1024 else: if data_args.block_size > tokenizer.model_max_length: logger.warning( f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model" f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." ) block_size = min(data_args.block_size, tokenizer.model_max_length) # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict. # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. total_length = (total_length // block_size) * block_size # Split by chunks of max_len. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() 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"): if not data_args.streaming: lm_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 {block_size}", ) else: lm_datasets = tokenized_datasets.map( group_texts, batched=True, ) if training_args.do_train: if "train" not in tokenized_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = lm_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) if training_args.do_eval: if "validation" not in tokenized_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = lm_datasets["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) def preprocess_logits_for_metrics(logits, labels): if isinstance(logits, tuple): # Depending on the model and config, logits may contain extra tensors, # like past_key_values, but logits always come first logits = logits[0] return logits.argmax(dim=-1) metric = evaluate.load("accuracy") def compute_metrics(eval_preds): preds, labels = eval_preds # preds have the same shape as the labels, after the argmax(-1) has been calculated # by preprocess_logits_for_metrics but we need to shift the labels labels = labels[:, 1:].reshape(-1) preds = preds[:, :-1].reshape(-1) return metric.compute(predictions=preds, references=labels) # 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 will default to DataCollatorWithPadding, so we change it. data_collator=default_data_collator, compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None, preprocess_logits_for_metrics=preprocess_logits_for_metrics if training_args.do_eval and not is_torch_tpu_available() else None, ) transformers.utils.logging.set_verbosity(transformers.utils.logging.WARNING) # 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": "text-generation"} 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) if __name__ == "__main__": main()