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@@ -1,663 +1,113 @@#!/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 accelerate launch --mixed_precision bf16 finetune_bert.py --model_direction ltr --learning_rate 5e-5 --output_dir checkpoints/test """ # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. """ From https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py """ import logging import argparse import math import os import sys from dataclasses import dataclass, field from itertools import chain from typing import Optional import datasets import evaluate import accelerate 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_xla_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 from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm from transformers import get_scheduler from utils import preprocess_datasets, convert_to_torch_dataset, add_attn_hooks, causal_loss_wrapper # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.47.0.dev0") require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") 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: def parse_args(): """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. Re-using HuggingFace arguments when possible (most of the help strings are directly copied). https://github.com/huggingface/transformers/blob/7bbc62474391aff64f63fcc064c975752d1fa4de/examples/pytorch/language-modeling/run_clm.py#L75 """ # text_direction: str = field( # # ) 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)."}, ) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether to trust the execution of code from datasets/models defined on the Hub." " This option should only be set to `True` for repositories you trust and in which you have read the" " code, as it will execute code present on the Hub on your local machine." ) }, ) 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." ) }, ) parser = argparse.ArgumentParser() 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" ) # Model parser.add_argument("--model_direction", type=str, required=True, choices=["ltr", "rtl"], help="Whether to train a left-to-right or right-to-left LM.") parser.add_argument("--model_name_or_path", type=str, default="bert-base-uncased", help="Checkpoint to initialize weights from.") # TODO: option for training from scratch w/ conf @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)."}, ) 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." ) }, ) 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."} # Data parser.add_argument("--dataset_name", type=str, default="Salesforce/wikitext", help="The name of the dataset to use (via the datasets library).") parser.add_argument("--dataset_config_name", type=str, default="wikitext-103-v1", help="The configuration name of the dataset to use (via the datasets library).") # TODO: block_size, train on shorter seqs? parser.add_argument( "--block_size", type=int, 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)." ) 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." # Training parser.add_argument("--output_dir", type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") parser.add_argument("--per_device_train_batch_size", type=int, default=8) parser.add_argument("--per_device_eval_batch_size", type=int, default=16) parser.add_argument("--gradient_accumulation_steps", type=int, default=1) parser.add_argument("--num_train_epochs", type=int, default=1) parser.add_argument("--learning_rate", type=float, required=True) parser.add_argument("--warmup_steps", type=int, default=0) parser.add_argument("--weight_decay", type=float, default=0.0) parser.add_argument("--logging_steps", type=int, default=1, help="Number of update steps between two logs.") parser.add_argument("--eval_steps", type=int, default=500, help="Number of update steps between two logs.") parser.add_argument("--dataloader_num_workers", type=int, default=8) return parser.parse_args() 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() # 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: {training_args.parallel_mode.value == 'distributed'}, 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, token=model_args.token, streaming=data_args.streaming, trust_remote_code=model_args.trust_remote_code, ) 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, token=model_args.token, streaming=data_args.streaming, trust_remote_code=model_args.trust_remote_code, ) 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, token=model_args.token, streaming=data_args.streaming, trust_remote_code=model_args.trust_remote_code, ) 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, token=model_args.token, **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, token=model_args.token, **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, token=model_args.token, **dataset_args, ) args = parse_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. accelerator = accelerate.Accelerator() model = transformers.AutoModelForMaskedLM.from_pretrained(args.model_name_or_path, attn_implementation="sdpa") add_attn_hooks(model, args.model_direction) tokenizer = transformers.AutoTokenizer.from_pretrained(args.model_name_or_path) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. # Data raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) block_size = args.block_size if args.block_size is not None else model.config.max_position_embeddings processed_datasets = preprocess_datasets(raw_datasets, tokenizer, block_size) for split, hf_dataset in processed_datasets.items(): processed_datasets[split] = convert_to_torch_dataset(hf_dataset) config_kwargs = { "cache_dir": model_args.cache_dir, "revision": model_args.model_revision, "token": model_args.token, "trust_remote_code": model_args.trust_remote_code, } 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}") train_loader = DataLoader(processed_datasets["train"], batch_size=args.per_device_train_batch_size, shuffle=True) val_loader = DataLoader(processed_datasets["validation"], batch_size=args.per_device_eval_batch_size) # test_loader = DataLoader(processed_datasets["test"], batch_size=args.per_device_eval_batch_size) model, train_loader, val_loader = accelerator.prepare(model, train_loader, val_loader) tokenizer_kwargs = { "cache_dir": model_args.cache_dir, "use_fast": model_args.use_fast_tokenizer, "revision": model_args.model_revision, "token": model_args.token, "trust_remote_code": model_args.trust_remote_code, } 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, token=model_args.token, trust_remote_code=model_args.trust_remote_code, torch_dtype=torch_dtype, low_cpu_mem_usage=model_args.low_cpu_mem_usage, ) else: model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code) 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 hasattr(config, "max_position_embeddings"): max_pos_embeddings = config.max_position_embeddings else: # Define a default value if the attribute is missing in the config. max_pos_embeddings = 1024 if data_args.block_size is None: block_size = tokenizer.model_max_length if block_size > max_pos_embeddings: logger.warning( f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " f"Using block_size={min(1024, max_pos_embeddings)} instead. You can change that default value by passing --block_size xxx." ) if max_pos_embeddings > 0: block_size = min(1024, max_pos_embeddings) else: 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/process#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", cache_dir=model_args.cache_dir) 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, processing_class=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_xla_available() else None, preprocess_logits_for_metrics=preprocess_logits_for_metrics if training_args.do_eval and not is_torch_xla_available() else None, optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay) lr_scheduler = transformers.get_scheduler( name=transformers.SchedulerType.COSINE, optimizer=optimizer, num_warmup_steps=args.warmup_steps, #* accelerator.num_processes, num_training_steps=args.num_train_epochs * math.ceil(len(train_loader) / args.gradient_accumulation_steps), ) loss_fn = causal_loss_wrapper(args.model_direction) # 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 model.train() optimizer.zero_grad() metrics = train_result.metrics for epoch in range(args.num_train_epochs): for step, batch in enumerate(tqdm(train_loader)): labels = batch.pop("labels") outputs = model(**batch) loss = loss_fn(outputs.logits, labels) loss.backward() 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)) if (step + 1) % 50 == 1: print(f"{loss.item()=}") 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 (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() def _mp_fn(index): # For xla_spawn (TPUs) if __name__ == "__main__": main() if __name__ == "__main__": main()
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@@ -21,6 +21,10 @@ 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. """ From https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py """ import logging import math import os
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@@ -71,7 +75,9 @@ class ModelArguments:""" Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ # text_direction: str = field( # # ) model_name_or_path: Optional[str] = field( default=None, metadata={
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@@ -80,10 +86,10 @@ "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)}, ) # 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={
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@@ -654,4 +660,4 @@ main()if __name__ == "__main__": main() main()
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@@ -1,4 +1,4 @@accelerate datasets evaluate torch transformers
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@@ -1,45 +1,130 @@from itertools import chain import torch import torch.nn as nn import transformers from datasets import DatasetDict from transformers import PreTrainedTokenizer def ltr_mask(seq_len: int) -> torch.Tensor: mask = torch.ones((seq_len, seq_len), dtype=torch.bool) return torch.tril(mask, diagonal=-1) return torch.tril(mask) def rtl_mask(seq_len: int) -> torch.Tensor: return ltr_mask(seq_len).T def add_attn_hooks(model: transformers.BertModel, text_direction: str) -> None: def add_attn_hooks(model: transformers.BertModel, model_direction: str) -> None: """ Forces bidirectional `model` into a unidirectional one based on `direction`. Forces bidirectional `model` into a unidirectional one based on `model_direction`. Adds hooks to `model`'s self-attention blocks, in-place. Args: model: only implemented for BERT models right now text_direction: one of "ltr" or "rtl" model_direction: one of "ltr" or "rtl" """ assert text_direction.lower() in ("ltr", "rtl") mask_func = ltr_mask if text_direction.lower() == "ltr" else rtl_mask model.register_buffer("attn_mask", mask_func(model.config.max_position_embeddings).to(model.device)) assert model_direction.lower() in ("ltr", "rtl") mask_func = ltr_mask if model_direction.lower() == "ltr" else rtl_mask model.register_buffer("attention_mask", mask_func(model.config.max_position_embeddings).to(model.device)) def attn_hook(attn_module: nn.Module, args: tuple, kwargs: dict): """ Assuming https://github.com/huggingface/transformers/blob/33868a057c02f0368ba63bd1edb746be38fe3d90/src/transformers/models/bert/modeling_bert.py#L515 so no `kwargs` and `attention_mask` is second positional arg. Uses nonlocal `model.attn_mask` to save memory. Uses nonlocal `model.attention_mask` to save memory. """ assert not kwargs args = list(args) assert args[1].size()[-2:] == model.attn_mask.size(), f"{args[1].size()=} {model.attn_mask.size()=}" args[1] = model.attn_mask seq_len = args[0].size(1) # During training, we should always be padding to max length, so we can always use `model.attention_mask`. if seq_len != model.config.max_position_embeddings: assert not torch.is_grad_enabled() attention_mask = ltr_mask(seq_len).to(model.device) else: attention_mask = model.attention_mask args[1] = attention_mask return tuple(args), kwargs for name, module in model.named_modules(): if isinstance(module, transformers.models.bert.modeling_bert.BertSelfAttention): module._forward_hooks.clear() # in case we run multiple times module._forward_pre_hooks.clear() # in case we run multiple times module.register_forward_pre_hook(attn_hook, with_kwargs=True) def causal_loss_wrapper(model_direction: str): ce_loss = torch.nn.CrossEntropyLoss() def loss_fn(logits, labels): if model_direction.lower() == "ltr": shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() elif model_direction.lower() == "rtl": shift_logits = logits[..., 1:, :].contiguous() shift_labels = labels[..., :-1].contiguous() else: raise NotImplementedError(f"{model_direction=}") # Flatten the tokens return ce_loss(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) return loss_fn def preprocess_datasets(raw_datasets: DatasetDict, tokenizer: PreTrainedTokenizer, block_size: int) -> DatasetDict: """ Preprocess datasets. Closely follows https://github.com/huggingface/transformers/blob/7bbc62474391aff64f63fcc064c975752d1fa4de/examples/pytorch/language-modeling/run_clm.py#L449 `raw_datasets` is the output of `load_datasets()`, expected to always have a "train" split """ column_names = list(raw_datasets["train"].features) text_column_name = "text" if "text" in column_names else column_names[0] tokenized_datasets = raw_datasets.map( lambda examples: tokenizer(examples[text_column_name]), batched=True, num_proc=8, remove_columns=column_names, desc="Running tokenizer on dataset", ) # 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/process#map # # with training_args.main_process_first(desc="grouping texts together"): return tokenized_datasets.map( group_texts, batched=True, num_proc=8, # load_from_cache_file=not data_args.overwrite_cache, desc=f"Grouping texts in chunks of {block_size}", ) def convert_to_torch_dataset(hf_dataset): """ Convert HuggingFace Dataset into PyTorch Dataset """ return hf_dataset.with_format("torch")
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