Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. 这个问题怎么解决

To solve the issue with modules being dispatched on the CPU or disk and ensure you have enough GPU RAM to fit the quantized model, you can follow these steps:

1. Set `load_in_8bit_fp32_cpu_offload=True` when loading the pretrained model using `from_pretrained`.

model = GPT2LMHeadModel.from_pretrained(
    model_path,
    load_in_8bit_fp32_cpu_offload=True,
)

2. Pass a custom `device_map` to `from_pretrained` to specify the devices where you want to load the model and its modules.

model = GPT2LMHeadModel.from_pretrained(
    model_path,
    load_in_8bit_fp32_cpu_offload=True,
    device_map={"cuda:0": "cuda:1"}  # Custom device map
)

In the code above, `model_path` should be the path to the directory containing the pretrained model. The `load_in_8bit_fp32_cpu_offload` parameter is set to `True` to enable loading the model in 8-bit floating-point format with CPU offloading. The `device_map` parameter is used to specify a custom mapping of devices, in this case mapping `cuda:0` to `cuda:1`.

By enabling CPU offloading and using a custom device map, you can distribute the computation between CPU and GPU, allowing for better memory utilization. Ensure that you have enough GPU RAM available to fit the quantized model by managing your GPU memory usage and considering model size and other memory-consuming operations in your code.