此外，如果需要在 cpu 上运行量化后的模型，还需要安装 `gcc` 与 `openmp`。多数 Linux 发行版默认已安装。对于 Windows ，可在安装 [TDM-GCC](https://jmeubank.github.io/tdm-gcc/) 时勾选 `openmp`。 Windows 测试环境 `gcc` 版本为 `TDM-GCC 10.3.0`， Linux 为 `gcc 11.3.0`。
此外，如果需要在 cpu 上运行量化后的模型，还需要安装 `gcc` 与 `openmp`。多数 Linux 发行版默认已安装。对于 Windows ，可在安装 [TDM-GCC](https://jmeubank.github.io/tdm-gcc/) 时勾选 `openmp`。 Windows 测试环境 `gcc` 版本为 `TDM-GCC 10.3.0`， Linux 为 `gcc 11.3.0`。在 MacOS 上请参考 [Q1](FAQ.md#q1)。
### 代码调用
@ -191,48 +191,20 @@ model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True
@ -188,54 +188,21 @@ model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=Tru
If your encounter the error `Could not find module 'nvcuda.dll'` or `RuntimeError: Unknown platform: darwin`(MacOS), please [load the model locally](README_en.md#load-the-model-locally).
### CPU Deployment on Mac
The default Mac enviroment does not support Openmp. One may encounter such warning/errors when execute the `AutoModel.from_pretrained(...)` command `clang: error: unsupported option '-fopenmp'`.
Take the quantified int4 version [chatglm-6b-int4](https://huggingface.co/THUDM/chatglm-6b-int4) for example, two extra steps are needed.
#### STEP 1: Install `libomp`
```bash
# STEP 1: install libopenmp, check `https://mac.r-project.org/openmp/` for details.
# Assumption: `gcc(clang) >= 14.x`, read the R-Poject before run the code:
sudo tar fvxz openmp-14.0.6-darwin20-Release.tar.gz -C /
```
Four files (`/usr/local/lib/libomp.dylib`, `/usr/local/include/ompt.h`, `/usr/local/include/omp.h`, `/usr/local/include/omp-tools.h`) are installed accordingly.
#### STEP 2: Configure `gcc` with `-fopenmp`
Next, modify the [quantization.py](https://huggingface.co/THUDM/chatglm-6b-int4/blob/main/quantization.py) file of the `chatglm-6b-int4` project. In the file, change the `gcc -O3 -fPIC -pthread -fopenmp -std=c99` configuration to `gcc -O3 -fPIC -Xclang -fopenmp -pthread -lomp -std=c99` (check the corresponding python code [HERE](https://huggingface.co/THUDM/chatglm-6b-int4/blob/63d66b0572d11cedd5574b38da720299599539b3/quantization.py#L168)), i.e.:
```python
# STEP 2: Change the line contains `gcc -O3 -fPIC -pthread -fopenmp -std=c99` to:
> Notice: `platform.uname()[0] == 'Darwin'` could be used to determine the OS type and further polish the python script.
> Notice: If you have executed the `ChatGLM` project and failed, you may want to clean the cache of Huggingface before your next try, i.e. `rm -rf ${HOME}/.cache/huggingface/modules/transformers_modules/chatglm-6b-int4`. Since `rm` is used, please MAKE SURE that the command deletes the right files.
### GPU Inference on Mac
For Macs (and MacBooks) with Apple Silicon, it is possible to use the MPS backend to run ChatGLM-6B on the GPU. First, you need to refer to Apple's [official instructions](https://developer.apple.com/metal/pytorch) to install PyTorch-Nightly.
### Inference on Mac
For Macs (and MacBooks) with Apple Silicon, it is possible to use the MPS backend to run ChatGLM-6B on the GPU. First, you need to refer to Apple's [official instructions](https://developer.apple.com/metal/pytorch) to install PyTorch-Nightly. (The correct version number should be 2.1.0.dev2023xxxx, not 2.0.0).
Currently you must [load the model locally](README_en.md#load-the-model-locally) on MacOS. Change the code to load the model from your local path, and use the mps backend:
```python
model = AutoModel.from_pretrained("your local path", trust_remote_code=True).half().to('mps')
```
For Mac users with Mac OS >= 13.3, one may encounter errors related to the `half()` method. Use the `float()` method instead:
Loading a FP16 ChatGLM-6B model requires about 13GB of memory. Machines with less memory (such as a MacBook Pro with 16GB of memory) will use the virtual memory on the hard disk when there is insufficient free memory, resulting in a serious slowdown in inference speed. At this time, a quantized model such as chatglm-6b-int4 can be used. Because the quantized kernel on the GPU is written in CUDA, it cannot be used on MacOS, and can only be inferred using the CPU:
```python
model = AutoModel.from_pretrained("your local path", trust_remote_code=True).float().to('mps')
# For INT8-quantized model, change "chatglm-6b-int4" to "chatglm-6b-int8"
model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).float()
```
Then you can use GPU-accelerated model inference on Mac.
> Notice: there is no promblem to run the non-quantified version of ChatGLM with MPS. One could check [this issue](https://github.com/THUDM/ChatGLM-6B/issues/462) to run the quantified version with MPS as the backend (and get `ERRORS`). Unpacking [kernel](https://huggingface.co/THUDM/chatglm-6b/blob/658202d88ac4bb782b99e99ac3adff58b4d0b813/quantization.py#L27) as an `ELF` file shows its backend is `cuda`, which fails on MPS currently (`torch 2.1.0.dev20230502`).
### Multi-GPU Deployment
If you have multiple GPUs, but the memory size of each GPU is not sufficient to accommodate the entire model, you can split the model across multiple GPUs.
duzx16
2 years ago