llava预训练报错RuntimeError: The size of tensor a (0) must match the size of tensor b (592) at non-singleton dimension 2

### 复现方式
`xtuner train llava_internlm2_chat_20b_clip_vit_large_p14_336_e1_gpu8_pretrain.py`

### 配置文件
仅改动数据集及模型位置

### 运行日志
```
Map (num_proc=32): 100%|██████████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:01<00:00, 14362.05 examples/s]
Map (num_proc=32): 100%|██████████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:01<00:00, 11732.15 examples/s]
Filter (num_proc=32): 100%|███████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:00<00:00, 17789.18 examples/s]
Map (num_proc=32): 100%|████████████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:15<00:00, 951.24 examples/s]
Filter (num_proc=32): 100%|███████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:01<00:00, 13716.77 examples/s]
Map (num_proc=32): 100%|████████████████████████████████████████████████████████████████████████████████████| 15000/15000 [00:15<00:00, 986.93 examples/s]
07/13 10:44:00 - mmengine - WARNING - Dataset LLaVADataset has no metainfo. ``dataset_meta`` in visualizer will be None.
07/13 10:44:00 - mmengine - INFO - Num train samples 15000
07/13 10:44:00 - mmengine - INFO - train example:
07/13 10:44:00 - mmengine - INFO - <|im_start|>user

Render a clear and concise summary of the photo.<|im_end|>
<|im_start|>assistant
select luxury furniture 3 - inch gel memory foam mattress topper<|im_end|>

07/13 10:44:00 - mmengine - INFO - before_train in EvaluateChatHook.
You are using an old version of the checkpointing format that is deprecated (We will also silently ignore `gradient_checkpointing_kwargs` in case you passed it).Please update to the new format on your modeling file. To use the new format, you need to completely remove the definition of the method `_set_gradient_checkpointing` in your model.
torch.Size([1, 0, 8, 8, 128])
torch.Size([1, 0, 48, 128])
torch.Size([1, 0, 48, 128])
torch.Size([1, 48, 0, 128])
torch.Size([1, 48, 0, 128])
torch.Size([1, 1, 592, 128])
torch.Size([1, 1, 592, 128])
Traceback (most recent call last):
File "/data/sx/xtuner/xtuner/tools/train.py", line 360, in
main()
File "/data/sx/xtuner/xtuner/tools/train.py", line 356, in main
runner.train()
File "/usr/local/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1777, in train
model = self.train_loop.run() # type: ignore
File "/usr/local/lib/python3.10/site-packages/mmengine/runner/loops.py", line 271, in run
self.runner.call_hook('before_train')
File "/usr/local/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1839, in call_hook
getattr(hook, fn_name)(self, **kwargs)
File "/data/sx/xtuner/xtuner/engine/hooks/evaluate_chat_hook.py", line 234, in before_train
self._generate_samples(runner, max_new_tokens=50)
File "/data/sx/xtuner/xtuner/engine/hooks/evaluate_chat_hook.py", line 220, in _generate_samples
self._eval_images(runner, model, device, max_new_tokens,
File "/data/sx/xtuner/xtuner/engine/hooks/evaluate_chat_hook.py", line 152, in _eval_images
generation_output = model.generate(
File "/usr/local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/transformers/generation/utils.py", line 1914, in generate
result = self._sample(
File "/usr/local/lib/python3.10/site-packages/transformers/generation/utils.py", line 2651, in _sample
outputs = self(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/root/.cache/huggingface/modules/transformers_modules/modeling_internlm2.py", line 1204, in forward
outputs = self.model(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/root/.cache/huggingface/modules/transformers_modules/modeling_internlm2.py", line 1004, in forward
layer_outputs = decoder_layer(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/root/.cache/huggingface/modules/transformers_modules/modeling_internlm2.py", line 738, in forward
hidden_states, self_attn_weights, present_key_value = self.attention(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/data/sx/xtuner/xtuner/model/modules/dispatch/internlm2.py", line 107, in internlm2_attn_forward
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
File "/data/sx/xtuner/xtuner/model/modules/dispatch/internlm2.py", line 29, in apply_rotary_pos_emb
q_embed = (q * cos) + (rotate_half(q) * sin)
RuntimeError: The size of tensor a (0) must match the size of tensor b (592) at non-singleton dimension 2

```