Some questions about finetuning

[victorup]

Hi,
I would like to finetune CogView2 on my own dataset. I used cogdata to process the datasets with a JSON file {'img1':'text1'; 'img2': 'text2', ...} and a tar file which includes images in the keys of the JSON file.

When I run pretrain_coglm.py, an error occurs:

File "pretrain_coglm.py", line 210, in forward_step
    tokens, position_ids, labels, attention_mask, loss_mask = get_batch(
  File "pretrain_coglm.py", line 61, in get_batch
    raise ValueError('temporally not support pure image samples')

I commented out the raise line and encountered another error:

File "pretrain_coglm.py", line 214, in forward_step
    logits, *mems = model(tokens, position_ids, attention_mask)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/torch/nn/modules/mo
dule.py", line 1102, in _call_impl                                                                
    return forward_call(*input, **kwargs)                                                         
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/deepspeed/utils/nvt
x.py", line 11, in wrapped_fn
    return func(*args, **kwargs)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/deepspeed/runtime/engine.py", line 1568, in forward
    loss = self.module(*inputs, **kwargs)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/SwissArmyTransformer/model/base_model.py", line 111, in forward
    return self.transformer(*args, **kwargs)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/SwissArmyTransformer/model/transformer.py", line 411, in forward
    hidden_states = HOOKS_DEFAULT['word_embedding_forward'](self, input_ids, output_cross_layer=output_cross_layer,**kw_args)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/SwissArmyTransforme
r/transformer_defaults.py", line 117, in word_embedding_forward_default
    return self.transformer.word_embeddings(input_ids)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/torch/nn/modules/mo
dule.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/SwissArmyTransforme
r/mpu/layers.py", line 121, in forward
    output_parallel = F.embedding(masked_input, self.weight,
  File "/home/xinpeng/miniconda3/envs/cogview_py38/lib/python3.8/site-packages/torch/nn/functional
.py", line 2044, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: 'weight' must be 2-D

My environment is as follows:

• python==3.8.0
• torch==1.10.0+cu111
• ipython==7.21.0
• deepspeed==0.6.3
• SwissArmyTransformer==0.2.1
• icetk==0.0.7
• sentencepiece==0.1.98

Additionally, I would like to know if there is a command to resume the checkpoint you provided for further fine-tuning. How can I do that?"

Thank you!