| |
| import numpy as np |
| import os |
| from datasets import load_from_disk |
|
|
| from swift.dataset import DatasetSyntax, sample_dataset |
| from swift.template import update_generation_config_eos_token |
| from swift.tuner_plugin import tuners_map |
| from swift.tuners import Swift |
| from swift.utils import get_logger |
|
|
| logger = get_logger() |
|
|
|
|
| def prepare_adapter(args, model, adapters=None): |
| if args.tuner_backend == 'unsloth': |
| if args.model_meta.is_multimodal: |
| from unsloth import FastVisionModel as UnslothModel |
| else: |
| from unsloth import FastLanguageModel as UnslothModel |
| UnslothModel.for_inference(model) |
| return model |
| if args.tuner_type in tuners_map: |
| tuner = tuners_map[args.tuner_type] |
| else: |
| tuner = Swift |
| |
| adapters = adapters if adapters is not None else args.adapters |
| for adapter in adapters: |
| model = tuner.from_pretrained(model, adapter) |
| if args.tuner_type == 'bone': |
| |
| model.to(model.dtype) |
| return model |
|
|
|
|
| def prepare_model_template(args, **kwargs): |
| adapters = kwargs.get('adapters') |
| model, processor = args.get_model_processor(**kwargs) |
| template = args.get_template(processor) |
| if model is not None: |
| if template.use_model: |
| template.model = model |
| model = prepare_adapter(args, model, adapters=adapters) |
| if args.task_type == 'causal_lm': |
| update_generation_config_eos_token(model.generation_config, template) |
| return model, template |
|
|
|
|
| def _select_dataset(args, dataset): |
| if 'length' in dataset.column_names and 'lengths' not in dataset.column_names: |
| |
| dataset = dataset.rename_column('length', 'lengths') |
| max_length = args.max_length |
| if args.truncation_strategy == 'delete': |
| lengths = dataset['lengths'] |
| idxs = [ |
| i for i, length in enumerate(lengths) if (max(length) if isinstance(length, list) else length) <= max_length |
| ] |
| new_dataset = dataset.select(idxs) |
| else: |
| new_dataset = dataset |
| if len(new_dataset) < len(dataset): |
| logger.info(f'Dataset filtered, origin length: {len(dataset)}, filtered dataset length: {len(new_dataset)}') |
| return new_dataset |
|
|
|
|
| def get_cached_dataset(args): |
| train_datasets, val_datasets = [], [] |
| random_state = np.random.RandomState(args.data_seed) |
| for cached_dataset, datasets in zip([args.cached_dataset, args.cached_val_dataset], [train_datasets, val_datasets]): |
| for path in cached_dataset: |
| if os.path.exists(path): |
| dataset_sample = None |
| else: |
| path, dataset_sample = DatasetSyntax._safe_split(path, '#', True, 'right') |
| dataset = _select_dataset(args, load_from_disk(path)) |
| if dataset_sample is not None: |
| dataset = sample_dataset( |
| dataset, int(dataset_sample), args.dataset_shuffle, random_state=random_state, shuffle_all=True) |
| datasets.append(dataset) |
| return train_datasets, val_datasets |
|
|
