It is recommended to support multiple GPU cards about chatglm3-finetune

[xxw1995]

显卡资源不够 后续会增加deepspeed或者megatronLM的代码

[xxw1995]

Traceback (most recent call last): File "finetune.py", line 70, in main() File "finetune.py", line 55, in main model = get_peft_model(model, peft_config).to("cuda") File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 989, in to return self._apply(convert) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 641, in _apply module._apply(fn) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 641, in _apply module._apply(fn) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 641, in _apply module._apply(fn) [Previous line repeated 5 more times] File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 664, in _apply param_applied = fn(param) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 987, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 72.00 MiB (GPU 0; 22.03 GiB total capacity; 20.75 GiB already allocated; 56.88 MiB free; 21.26 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

拉取最新的代码，已支持deepspeed