Make dataset and collator for supervised fine-tuning.
(
tokenizer: transformers.PreTrainedTokenizer,
data_args,
transform,
data_collator=None,
llm_type="minicpm",
slice_config=None,
patch_size=14,
query_nums=64,
batch_vision=False,
max_length=2048,
)
| 84 | |
| 85 | |
| 86 | def make_supervised_data_module( |
| 87 | tokenizer: transformers.PreTrainedTokenizer, |
| 88 | data_args, |
| 89 | transform, |
| 90 | data_collator=None, |
| 91 | llm_type="minicpm", |
| 92 | slice_config=None, |
| 93 | patch_size=14, |
| 94 | query_nums=64, |
| 95 | batch_vision=False, |
| 96 | max_length=2048, |
| 97 | ) -> Dict: |
| 98 | """Make dataset and collator for supervised fine-tuning.""" |
| 99 | dataset_cls = SupervisedDataset |
| 100 | |
| 101 | rank0_print("Loading data...") |
| 102 | |
| 103 | def load(path): |
| 104 | if not path.endswith(('.json', '.jsonl')): |
| 105 | raise ValueError('need .json or .jsonl') |
| 106 | with open(path, encoding='utf-8') as f: |
| 107 | return json.load(f) if path.endswith('.json') else [json.loads(l) for l in f if l.strip()] |
| 108 | train_json = load(data_args.data_path) |
| 109 | |
| 110 | train_dataset = dataset_cls( |
| 111 | train_json, |
| 112 | transform, |
| 113 | tokenizer, |
| 114 | slice_config=slice_config, |
| 115 | llm_type=llm_type, |
| 116 | patch_size=patch_size, |
| 117 | query_nums=query_nums, |
| 118 | batch_vision=batch_vision, |
| 119 | max_length=max_length, |
| 120 | max_line_res=data_args.max_line_res |
| 121 | ) |
| 122 | |
| 123 | if data_args.eval_data_path: |
| 124 | eval_json = load(data_args.eval_data_path) |
| 125 | eval_dataset = dataset_cls( |
| 126 | eval_json, |
| 127 | transform, |
| 128 | tokenizer, |
| 129 | slice_config=slice_config, |
| 130 | llm_type=llm_type, |
| 131 | patch_size=patch_size, |
| 132 | query_nums=query_nums, |
| 133 | batch_vision=batch_vision, |
| 134 | max_length=max_length, |
| 135 | max_line_res=data_args.max_line_res |
| 136 | ) |
| 137 | else: |
| 138 | eval_dataset = None |
| 139 | |
| 140 | return dict( |
| 141 | train_dataset=train_dataset, |
| 142 | eval_dataset=eval_dataset, |
| 143 | data_collator= partial(data_collator, max_length=max_length), |
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