RuntimeError: self and mat2 must have the same dtype

[baibaiw5]

---------pip package version
transformers-4.30.0.dev0
accelerate 0.20.0.dev0
bitsandbytes 0.39.0
peft-0.3.0.dev0

----------python cmd
python qlora.py --model_name_or_path /home/bmb/models/facebook/opt-125m

---------error
Traceback (most recent call last):
File "/home/bmb/projects/qlora/qlora.py", line 766, in
train()
File "/home/bmb/projects/qlora/qlora.py", line 728, in train
train_result = trainer.train(resume_from_checkpoint=checkpoint_dir)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/trainer.py", line 1696, in train
return inner_training_loop(
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/trainer.py", line 1972, in _inner_training_loop
tr_loss_step = self.training_step(model, inputs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/trainer.py", line 2786, in training_step
loss = self.compute_loss(model, inputs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/trainer.py", line 2818, in compute_loss
outputs = model(**inputs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/peft/peft_model.py", line 678, in forward
return self.base_model(
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
output = old_forward(*args, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 945, in forward
outputs = self.model.decoder(
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
output = old_forward(*args, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 703, in forward
layer_outputs = torch.utils.checkpoint.checkpoint(
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 249, in checkpoint
return CheckpointFunction.apply(function, preserve, *args)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 107, in forward
outputs = run_function(*args)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 699, in custom_forward
return module(*inputs, output_attentions, None)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
output = old_forward(*args, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 331, in forward
hidden_states, self_attn_weights, present_key_value = self.self_attn(
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
output = old_forward(*args, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 174, in forward
query_states = self.q_proj(hidden_states) * self.scaling
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bmb/anaconda3/envs/qlora/lib/python3.10/site-packages/peft/tuners/lora.py", line 565, in forward
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
RuntimeError: self and mat2 must have the same dtype

[taishan1994]

pip install -q -U git+https://github.com/huggingface/peft.git
peft version should be 0.4.0.dev0

[baibaiw5]

pip install -q -U git+https://github.com/huggingface/peft.git peft version should be 0.4.0.dev0

I used to install 0.4.0.dev0 and I meet the following problem: lora weights are not saved correctly
#41

[taishan1994]

pip install -q -U git+https://github.com/huggingface/peft.git peft version should be 0.4.0.dev0

I used to install 0.4.0.dev0 and I meet the following problem: lora weights are not saved correctly #41

maybe you can try this: #44

[baibaiw5]

peft version should be 0.4.0.dev0

Thank you.Now the checkpoint can be saved correctly