chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,82 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+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
+    # compat deploy
+    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':
+        # Bone has a problem of float32 matmul with bloat16 in `peft==0.14.0`
+        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:
+        # Compatible with ms-swift 3.x cache_dataset
+        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