#!/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 import sys from dataclasses import dataclass, field from typing import Optional from pathlib import Path import datasets import torch from build_dataset import build_instruction_dataset, DataCollatorForSupervisedDataset import transformers from transformers import ( CONFIG_MAPPING, AutoConfig, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import send_example_telemetry from transformers.utils.versions import require_version from peft import LoraConfig, TaskType, get_peft_model, PeftModel, get_peft_model_state_dict from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR IGNORE_INDEX = -100 DEFAULT_PAD_TOKEN = "[PAD]" DEFAULT_EOS_TOKEN = "" DEFAULT_BOS_TOKEN = "" DEFAULT_UNK_TOKEN = "" require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") class SavePeftModelCallback(transformers.TrainerCallback): def save_model(self, args, state, kwargs): if state.best_model_checkpoint is not None: checkpoint_folder = os.path.join(state.best_model_checkpoint, "sft_lora_model") else: checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}") peft_model_path = os.path.join(checkpoint_folder, "sft_lora_model") kwargs["model"].save_pretrained(peft_model_path) if "tokenizer" in kwargs: kwargs["tokenizer"].save_pretrained(peft_model_path) else: kwargs["processing_class"].save_pretrained(peft_model_path) def on_save(self, args, state, control, **kwargs): self.save_model(args, state, kwargs) return control def on_train_end(self, args, state, control, **kwargs): peft_model_path = os.path.join(args.output_dir, "sft_lora_model") kwargs["model"].save_pretrained(peft_model_path) if "tokenizer" in kwargs: kwargs["tokenizer"].save_pretrained(peft_model_path) else: kwargs["processing_class"].save_pretrained(peft_model_path) @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." ) }, ) tokenizer_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The tokenizer for weights initialization.Don't set if you want to train a model from scratch." ) }, ) 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)." ) }, ) 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"], }, ) 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_dir: Optional[str] = field( default=None, metadata={"help": "The 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[float] = field( default=0.05, 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_cache_dir: Optional[str] = field(default=None, metadata={"help": "The datasets processed stored"}) max_seq_length: Optional[int] = field(default=512) @dataclass class MyTrainingArguments(TrainingArguments): trainable : Optional[str] = field(default="q_proj,v_proj") lora_rank : Optional[int] = field(default=8) lora_dropout : Optional[float] = field(default=0.1) lora_alpha : Optional[float] = field(default=32.) modules_to_save : Optional[str] = field(default=None) peft_path : Optional[str] = field(default=None) force_resize_embeddings: bool = field(default=False) logger = logging.getLogger(__name__) def main(): parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments)) 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() 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", level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN, 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() # transformers.tokenization_utils.logging.set_verbosity_warning() # 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}" ) # 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) 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.tokenizer_name_or_path: tokenizer = LlamaTokenizer.from_pretrained(model_args.tokenizer_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 tokenizer.pad_token is None: print(f"Adding pad token {DEFAULT_PAD_TOKEN}") tokenizer.add_special_tokens(dict(pad_token=DEFAULT_PAD_TOKEN)) data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) eval_dataset=None train_dataset = None if training_args.do_train: with training_args.main_process_first(desc="loading and tokenization"): path = Path(data_args.dataset_dir) files = [os.path.join(path,file.name) for file in path.glob("*.json")] logger.info(f"Training files: {' '.join(files)}") train_dataset = build_instruction_dataset( data_path=files, tokenizer=tokenizer, max_seq_length=data_args.max_seq_length, data_cache_dir = None, preprocessing_num_workers = data_args.preprocessing_num_workers) logger.info(f"Num train_samples {len(train_dataset)}") logger.info("training example:") logger.info(tokenizer.decode(train_dataset[0]['input_ids'])) if training_args.do_eval: with training_args.main_process_first(desc="loading and tokenization"): files = [data_args.validation_file] logger.info(f"Evaluation files: {' '.join(files)}") eval_dataset = build_instruction_dataset( data_path=files, tokenizer=tokenizer, max_seq_length=data_args.max_seq_length, data_cache_dir = None, preprocessing_num_workers = data_args.preprocessing_num_workers) logger.info(f"Num eval_samples {len(eval_dataset)}") logger.info("eval example:") logger.info(tokenizer.decode(eval_dataset[0]['input_ids'])) 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 = LlamaForCausalLM.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=True ) 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") logger.info(f"len(tokenizer):{len(tokenizer)}") embedding_size = model.get_input_embeddings().weight.shape[0] if len(tokenizer) != embedding_size: logger.info("resize the embedding size by the size of the tokenizer") model.resize_token_embeddings(len(tokenizer)) if training_args.peft_path is not None: logger.info("Peft from pre-trained model") model = PeftModel.from_pretrained(model, training_args.peft_path) else: logger.info("Init new peft model") target_modules = training_args.trainable.split(',') modules_to_save = training_args.modules_to_save if modules_to_save is not None: modules_to_save = modules_to_save.split(',') lora_rank = training_args.lora_rank lora_dropout = training_args.lora_dropout lora_alpha = training_args.lora_alpha logger.info(f"target_modules: {target_modules}") logger.info(f"lora_rank: {lora_rank}") peft_config = LoraConfig( task_type=TaskType.CAUSAL_LM, target_modules=target_modules, inference_mode=False, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, modules_to_save=modules_to_save) model = get_peft_model(model, peft_config) #model.base_model.tie_weights() model.print_trainable_parameters() logger.info(f"model.modules_to_save: {model.modules_to_save}") old_state_dict = model.state_dict model.state_dict = ( lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict()) ).__get__(model, type(model)) # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, data_collator=data_collator, ) trainer.add_callback(SavePeftModelCallback) # 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) metrics = train_result.metrics metrics["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() metrics["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) if __name__ == "__main__": main()