diff --git a/llmc/data/dataset/base_dataset.py b/llmc/data/dataset/base_dataset.py
index 27753707..b135ff49 100644
--- a/llmc/data/dataset/base_dataset.py
+++ b/llmc/data/dataset/base_dataset.py
@@ -76,178 +76,6 @@ def build_calib_dataset(self):
             else:
                 self.calib_dataset = load_from_disk(self.calib_dataset_path)
 
-    def get_calib_samples(self):
-        if self.calib_dataset_name == 'custom_txt' or self.calib_dataset_name == 'custom_mm':
-            samples = self.calib_dataset
-        else:
-            preproc = PREPROC_REGISTRY[self.preproc]
-            preproc_param_dict = {
-                'calib_dataset': self.calib_dataset,
-                'tokenizer': self.tokenizer,
-                'n_samples': self.n_samples,
-                'seq_len': self.seq_len
-            }
-            if self.preproc == 'txt_general_preproc':
-                preproc_param_dict['key'] = self.key
-            samples = preproc(**preproc_param_dict)
-        return samples
-
-    def get_pad_setting(self, length):
-        if self.tokenizer.padding_side == 'left':
-            return [length, 0]
-        elif self.tokenizer.padding_side == 'right':
-            return [0, length]
-        else:
-            raise Exception(f'Not support padding_side: {self.tokenizer.padding_side}.')
-
-    def txt_group_samples_with_mask(self, samples):
-        calib_samples = []
-        input_ids = []
-        attention_mask = []
-        pad_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.pad_token)
-        if self.calib_bs < 0:
-            samples_len = [sample.shape[-1] for sample in samples]
-            max_len = max(samples_len)
-            samples_tmp = []
-            attention_mask_tmp = []
-            for sample in samples:
-                samples_tmp.append(
-                    F.pad(
-                        sample,
-                        self.get_pad_setting(max_len - sample.shape[-1]),
-                        value=pad_token_id
-                    )
-                )
-                attention_mask_tmp.append(
-                    F.pad(
-                        torch.ones(1, sample.shape[-1], dtype=torch.int64),
-                        self.get_pad_setting(max_len - sample.shape[-1]),
-                        value=0
-                    )
-                )
-            batch_input_ids = torch.cat(samples_tmp, dim=0)
-            batch_attention_mask = torch.cat(attention_mask_tmp, dim=0)
-            calib_samples.append(
-                {'input_ids': batch_input_ids, 'attention_mask': batch_attention_mask}
-            )
-        elif self.calib_bs == 1:
-            input_ids = samples
-            attention_mask = [torch.ones(1, sample.shape[-1], dtype=torch.int64) for sample in samples] # noqa
-            for i in range(len(samples)):
-                calib_samples.append(
-                    {'input_ids': input_ids[i], 'attention_mask': attention_mask[i]}
-                )
-        elif self.calib_bs > 1:
-            for i in range(0, len(samples), self.calib_bs):
-                start = i
-                end = min(i + self.calib_bs, len(samples))
-                batch_samples = samples[start:end]
-                batch_samples_len = [sample.shape[-1] for sample in batch_samples]
-                batch_max_len = max(batch_samples_len)
-                samples_tmp = []
-                attention_mask_tmp = []
-                for sample in batch_samples:
-                    samples_tmp.append(
-                        F.pad(
-                            sample,
-                            self.get_pad_setting(batch_max_len - sample.shape[-1]),
-                            value=pad_token_id
-                        )
-                    )
-                    attention_mask_tmp.append(
-                        F.pad(
-                            torch.ones(1, sample.shape[-1], dtype=torch.int64),
-                            self.get_pad_setting(batch_max_len - sample.shape[-1]),
-                            value=0
-                        )
-                    )
-                batch_input_ids = torch.cat(samples_tmp, dim=0)
-                batch_attention_mask = torch.cat(attention_mask_tmp, dim=0)
-                calib_samples.append(
-                    {
-                        'input_ids': batch_input_ids,
-                        'attention_mask': batch_attention_mask
-                    }
-                )
-        return calib_samples
-
-    def txt_group_samples_wo_mask(self, samples):  # without mask
-        calib_samples = []
-        if self.calib_bs < 0:
-            batch = torch.cat(samples, dim=0)
-            calib_samples.append({'input_ids': batch})
-        elif self.calib_bs == 1:
-            for i in range(len(samples)):
-                calib_samples.append({'input_ids': samples[i]})
-        elif self.calib_bs > 1:
-            for i in range(0, len(samples), self.calib_bs):
-                start = i
-                end = min(i + self.calib_bs, len(samples))
-                batch = samples[start:end]
-                batch = torch.cat(batch, dim=0)
-                calib_samples.append({'input_ids': batch})
-        return calib_samples
-
-    def img_txt_group_samples_with_mask(self, samples):
-        calib_samples = []
-        if self.calib_bs < 0:
-            calib_samples.append(self.batch_process(samples, calib_or_eval='calib'))
-        elif self.calib_bs == 1:
-            calib_samples = [self.batch_process([sample], calib_or_eval='calib') for sample in samples] # noqa
-        elif self.calib_bs > 1:
-            for i in range(0, len(samples), self.calib_bs):
-                start = i
-                end = min(i + self.calib_bs, len(samples))
-                batch = samples[start:end]
-                calib_samples.append(self.batch_process(batch, calib_or_eval='calib'))
-        return calib_samples
-
-    def audio_txt_group_samples_with_mask(self, samples):
-        calib_samples = []
-        if self.calib_bs < 0:
-            calib_samples.append(self.batch_process(samples, calib_or_eval='calib'))
-        elif self.calib_bs == 1:
-            calib_samples = [self.batch_process([sample], calib_or_eval='calib') for sample in samples] # noqa
-        elif self.calib_bs > 1:
-            for i in range(0, len(samples), self.calib_bs):
-                start = i
-                end = min(i + self.calib_bs, len(samples))
-                batch = samples[start:end]
-                calib_samples.append(self.batch_process(batch, calib_or_eval='calib'))
-        return calib_samples
-
-    def audio_img_txt_group_samples_with_mask(self, samples):
-        calib_samples = []
-        if self.calib_bs < 0:
-            calib_samples.append(self.batch_process(samples, calib_or_eval='calib'))
-        elif self.calib_bs == 1:
-            calib_samples = [self.batch_process([sample], calib_or_eval='calib') for sample in samples] # noqa
-        elif self.calib_bs > 1:
-            for i in range(0, len(samples), self.calib_bs):
-                start = i
-                end = min(i + self.calib_bs, len(samples))
-                batch = samples[start:end]
-                calib_samples.append(self.batch_process(batch, calib_or_eval='calib'))
-        return calib_samples
-
-    def img_group_samples_wo_mask(self, samples):  # without mask
-        calib_samples = []
-        if self.calib_bs < 0:
-            batch = {'pixel_values': torch.cat([sample['pixel_values']
-                                                for sample in samples], dim=0)}
-            calib_samples.append(batch)
-        elif self.calib_bs == 1:
-            calib_samples = samples
-        elif self.calib_bs > 1:
-            for i in range(0, len(samples), self.calib_bs):
-                start = i
-                end = min(i + self.calib_bs, len(samples))
-                batch = samples[start:end]
-                batch = {'pixel_values': torch.cat([sample['pixel_values']
-                                                    for sample in batch], dim=0)}
-                calib_samples.append(batch)
-        return calib_samples
-
     def get_calib_model_inputs(self, samples):
         if not self.padding:
             assert not self.calib_dataset_name == 'custom_mm'