Source code for lmflow.pipeline.finetuner

#!/usr/bin/env python
# coding=utf-8
"""The Finetuner class simplifies the process of running finetuning process on a language model for a TunableModel instance with given dataset.
"""

import copy
import logging
import os
import sys
from typing import Any, Iterable, Optional, Tuple

import datasets
import transformers
import evaluate
from itertools import chain
from transformers import (
    Trainer,
    default_data_collator,
    set_seed,
)
from copy import deepcopy
from transformers import PreTrainedModel, TrainingArguments
from transformers.trainer_utils import get_last_checkpoint
from transformers.trainer_callback import (
    TrainerCallback,
    TrainerControl,
    TrainerState,
)
from transformers.utils import (
    is_sagemaker_mp_enabled,
    send_example_telemetry,
)
import numpy as np

import lmflow.optim.optimizers as optim
from lmflow.args import OptimizerNames
from lmflow.datasets.dataset import Dataset
from lmflow.pipeline.base_tuner import BaseTuner
from lmflow.pipeline.utils.peft_trainer import PeftTrainer, PeftSavingCallback


[docs] logger = logging.getLogger(__name__)
[docs] class Finetuner(BaseTuner): """ Initializes the `Finetuner` class with given arguments. Parameters ------------ model_args : ModelArguments object. Contains the arguments required to load the model. data_args : DatasetArguments object. Contains the arguments required to load the dataset. finetuner_args : FinetunerArguments object. Contains the arguments required to perform finetuning. args : Optional. Positional arguments. kwargs : Optional. Keyword arguments. """ def __init__(self, model_args, data_args, finetuner_args, *args, **kwargs):
[docs] self.model_args = model_args
[docs] self.data_args = data_args
[docs] self.finetuner_args = finetuner_args
# 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)], ) log_level = finetuner_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: {finetuner_args.local_rank}," f" device: {finetuner_args.device}," f" n_gpu: {finetuner_args.n_gpu}," f"distributed training: {bool(finetuner_args.local_rank != -1)}," f" 16-bits training: {finetuner_args.fp16}" ) logger.info(f"Training/evaluation parameters {finetuner_args}") # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(finetuner_args.output_dir) and finetuner_args.do_train and not finetuner_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(finetuner_args.output_dir) if last_checkpoint is None and len(os.listdir(finetuner_args.output_dir)) > 0: raise ValueError( f"Output directory ({finetuner_args.output_dir}) already" " exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and finetuner_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at" f" {last_checkpoint}. To avoid this behavior, change" " the `--output_dir` or add `--overwrite_output_dir` to" " train from scratch." )
[docs] self.last_checkpoint = last_checkpoint
# Set seed before initializing model. set_seed(finetuner_args.seed)
[docs] def group_text(self, tokenized_datasets, model_max_length): """ Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as a dictionary. """ data_args = self.data_args finetuner_args = self.finetuner_args if data_args.block_size is None: block_size = 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 > model_max_length: if self.model_args.truncate_to_model_max_length: logger.warning( f"The block_size passed ({data_args.block_size}) is larger" f" than the maximum length for the model" f"({model_max_length})." f" Using block_size={model_max_length}." f"If you would like to use a longer 'block_size' that is" f" longer than the maximum length supported by the model," f" you can override this behavior with" f"default with `--truncate_to_model_max_length False`." ) block_size = model_max_length else: logger.warning( f"The block_size passed ({data_args.block_size}) is larger" f"than the maximum length for the model" f"({model_max_length})." f"Using block_size={data_args.block_size}.") block_size = data_args.block_size else: block_size = data_args.block_size # 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, 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() } 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 finetuner_args.main_process_first(desc="grouping texts together"): group_batch_size = data_args.group_texts_batch_size if data_args.disable_group_texts: group_batch_size = 1 if not data_args.streaming: lm_datasets = tokenized_datasets.map( group_texts, batched=True, batch_size=group_batch_size, 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, batch_size=group_batch_size, ) return lm_datasets
[docs] def create_customized_optimizer(self, base_trainer_class, model_args): class CustomizedOptimTrainer(base_trainer_class): @staticmethod def get_optimizer_cls_and_kwargs( args: TrainingArguments, model: Optional[PreTrainedModel] = None, ) -> Tuple[Any, Any]: # parse args.optim_args optim_args = {} if args.customized_optim_args: for mapping in args.customized_optim_args.replace(" ", "").split(","): key, value = mapping.split("=") optim_args[key] = value optimizer_kwargs = {"lr": args.learning_rate} if args.customized_optim == OptimizerNames.DUMMY: optimizer_cls = optim.Dummy dummy_kwargs = { "betas": (args.optim_dummy_beta1, args.optim_dummy_beta2), } optimizer_kwargs.update(dummy_kwargs) elif args.customized_optim == OptimizerNames.ADABELIEF: optimizer_cls = optim.AdaBelief adabelief_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay) } optimizer_kwargs.update(adabelief_kwargs) elif args.customized_optim == OptimizerNames.ADABOUND: optimizer_cls = optim.AdaBound adabound_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay) } optimizer_kwargs.update(adabound_kwargs) elif args.customized_optim == OptimizerNames.LARS: optimizer_cls = optim.LARS lars_kwargs = { "momentum": (args.optim_momentum), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(lars_kwargs) elif args.customized_optim == OptimizerNames.LAMB: optimizer_cls = optim.Lamb lamb_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(lamb_kwargs) elif args.customized_optim == OptimizerNames.ADAMAX: optimizer_cls = optim.Adamax adamax_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(adamax_kwargs) elif args.customized_optim == OptimizerNames.NADAM: optimizer_cls = optim.NAdam nadam_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(nadam_kwargs) elif args.customized_optim == OptimizerNames.RADAM: optimizer_cls = optim.RAdam radam_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(radam_kwargs) elif args.customized_optim == OptimizerNames.ADAMP: optimizer_cls = optim.AdamP adamp_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(adamp_kwargs) elif args.customized_optim == OptimizerNames.SGDP: optimizer_cls = optim.SGDP sgdp_kwargs = { "momentum": (args.optim_momentum), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(sgdp_kwargs) elif args.customized_optim == OptimizerNames.YOGI: optimizer_cls = optim.Yogi yogi_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(yogi_kwargs) elif args.customized_optim == OptimizerNames.SOPHIA: optimizer_cls = optim.SophiaG sophia_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(sophia_kwargs) elif args.customized_optim == OptimizerNames.ADAM: optimizer_cls = optim.Adam adam_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), } optimizer_kwargs.update(adam_kwargs) elif args.customized_optim == OptimizerNames.NOVOGRAD: optimizer_cls = optim.NovoGrad novograd_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(novograd_kwargs) elif args.customized_optim == OptimizerNames.ADADELTA: optimizer_cls = optim.Adadelta adadelta_kwargs = { } optimizer_kwargs.update(adadelta_kwargs) elif args.customized_optim == OptimizerNames.ADAGRAD: optimizer_cls = optim.AdaGrad adagrad_kwargs = { } optimizer_kwargs.update(adagrad_kwargs) elif args.customized_optim == OptimizerNames.ADAMW_SCHEDULE_FREE: optimizer_cls = optim.AdamWScheduleFree adamw_schedule_free_kwargs = { "betas": (args.optim_beta1, args.optim_beta2), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(adamw_schedule_free_kwargs) elif args.customized_optim == OptimizerNames.SGD_SCHEDULE_FREE: optimizer_cls = optim.SGDScheduleFree sgd_schedule_free_kwargs = { "momentum": (args.optim_momentum), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(sgd_schedule_free_kwargs) elif args.customized_optim == OptimizerNames.ADAN: optimizer_cls = optim.Adan adan_kwargs = { "betas": (args.optim_beta1, args.optim_beta2, args.optim_beta3), "weight_decay": (args.optim_weight_decay), } optimizer_kwargs.update(adan_kwargs) else: raise ValueError( f"Trainer cannot instantiate unsupported optimizer: " f" {args.customized_optim}" ) return optimizer_cls, optimizer_kwargs def create_optimizer(self): opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model if self.optimizer is None: decay_parameters = self.get_decay_parameter_names(opt_model) optimizer_grouped_parameters = [ { "params": [ p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad) ], "weight_decay": self.args.weight_decay, }, { "params": [ p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad) ], "weight_decay": 0.0, }, ] optimizer_cls, optimizer_kwargs = CustomizedOptimTrainer.get_optimizer_cls_and_kwargs(self.args, opt_model) # Overwrite `params` in case it's created by # `get_optimizer_cls_and_kwargs` e.g. for GaLore optimizer. if "params" in optimizer_kwargs: optimizer_grouped_parameters = optimizer_kwargs.pop( "params" ) # For layer-wise dummy optimizers we overwrite # optimizer_grouped_parameters with `optimizer_dict` to # avoid arguments conflicts. if "optimizer_dict" in optimizer_kwargs: optimizer_grouped_parameters = optimizer_kwargs.pop( "optimizer_dict" ) self.optimizer = optimizer_cls( optimizer_grouped_parameters, **optimizer_kwargs ) if is_sagemaker_mp_enabled(): self.optimizer = smp.DistributedOptimizer(self.optimizer) return CustomizedOptimTrainer
[docs] def tune(self, model, dataset, transform_dataset_in_place=True, data_collator=None): """ Perform tuning for a model Parameters ------------ model : TunableModel object. TunableModel to perform tuning. dataset: dataset to train model. """ model_args = self.model_args data_args = self.data_args finetuner_args = self.finetuner_args if not transform_dataset_in_place: dataset = copy.deepcopy(dataset) # Tokenization and text grouping must be done in the main process if dataset.backend == "custom_multi_modal": dataset.backend_dataset.register_tokenizer( model.tokenizer, model.image_processor) lm_dataset = dataset else: with finetuner_args.main_process_first(desc="dataset map tokenization"): tokenized_dataset = model.tokenize(dataset) if data_args.disable_group_texts: lm_dataset = tokenized_dataset else: lm_dataset = self.group_text( tokenized_dataset, model_max_length=model.get_max_length(), ) train_dataset = lm_dataset.get_backend_dataset() logger.info(f"Number of train samples: {len(train_dataset)}") if finetuner_args.do_eval: eval_dataset_args = deepcopy(data_args) eval_dataset_args.dataset_path = finetuner_args.eval_dataset_path eval_dataset = Dataset(eval_dataset_args) with finetuner_args.main_process_first(desc="dataset map tokenization"): tokenized_dataset = model.tokenize(eval_dataset) if data_args.disable_group_texts: lm_dataset = tokenized_dataset else: lm_dataset = self.group_text( tokenized_dataset, model_max_length=model.get_max_length(), ) eval_dataset = lm_dataset.get_backend_dataset() logger.info(f"Number of eval samples: {len(eval_dataset)}") 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) if finetuner_args.do_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)) # Initialize our Trainer training_args = finetuner_args if model_args.use_lora: FinetuningTrainer = PeftTrainer trainer_callbacks = [PeftSavingCallback] else: FinetuningTrainer = Trainer trainer_callbacks = [] if data_collator is None: data_collator = default_data_collator if training_args.use_customized_optim: BaseTrainer = FinetuningTrainer FinetuningTrainer = self.create_customized_optimizer( BaseTrainer, model_args ) if training_args.use_lisa: class DynamicLayerActivationCallback(TrainerCallback): def __init__(self, n_layers, interval_steps, model): super().__init__() self.n_layers = n_layers self.interval_steps = interval_steps self.model = model # Determine the way to access layers based on the model type class_to_layers_map = { 'LlamaForCausalLM': 'model.model.layers', 'Qwen2ForCausalLM': 'model.model.layers', 'MistralForCausalLM': 'model.model.layers', 'MixtralForCausalLM': 'model.model.layers', 'GemmaForCausalLM': 'model.model.layers', 'GPT2LMHeadModel': 'model.transformer.h', } model_class_name = self.model.__class__.__name__ if model_class_name in class_to_layers_map: self.layers_attribute = class_to_layers_map[model_class_name] else: self.layers_attribute = training_args.lisa_layers_attribute self.total_layers = len(eval('self.' + self.layers_attribute)) # Dynamically execute to get the number of layers self.active_layers_indices = [] def freeze_all_layers(self): layers = eval('self.' + self.layers_attribute) # Dynamically execute to get layers for layer in layers: for param in layer.parameters(): param.requires_grad = False def on_step_begin(self, args, state, control, **kwargs): # Check if it's time to switch active layers, including at step 0 if state.global_step % self.interval_steps == 0: self.switch_active_layers() def switch_active_layers(self): # First, disable gradients for all layers self.freeze_all_layers() # Randomly select n_layers to activate layers = eval('self.' + self.layers_attribute) # Re-fetch layer references self.active_layers_indices = np.random.choice(range(self.total_layers), self.n_layers, replace=False) print(f"Activating layers at indices: {self.active_layers_indices} for the next steps.", flush=True) # Enable gradients only for the selected layers for idx in self.active_layers_indices: for param in layers[idx].parameters(): param.requires_grad = True # Instantiate the callback dynamic_layer_activation_callback = DynamicLayerActivationCallback( n_layers=training_args.lisa_activated_layers, # Number of layers to activate interval_steps=training_args.lisa_interval_steps, # Step interval to update active layers model=model.get_backend_model() ) trainer_callbacks.append(dynamic_layer_activation_callback) trainer = FinetuningTrainer( model=model.get_backend_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=model.get_tokenizer(), # Data collator will default to DataCollatorWithPadding, so we change it. data_collator=data_collator, compute_metrics=compute_metrics if training_args.do_eval else None, preprocess_logits_for_metrics=preprocess_logits_for_metrics if training_args.do_eval else None, callbacks=trainer_callbacks ) # Training if training_args.do_train: checkpoint = None last_checkpoint = self.last_checkpoint 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) if not model_args.use_lora: trainer.save_model() # Saves the tokenizer too for easy upload else: if model_args.save_aggregated_lora: model.merge_lora_weights() model.save(finetuner_args.output_dir, model_args.save_aggregated_lora) # save language_projection for multi-modal model; if self.finetuner_args.save_language_projection: language_projection_state = trainer.model.language_projection.state_dict() torch.save( osp.join( self.finetuner_args.output_dir, "language_projection.pth"), language_projection_state) 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() 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) return model