[Document] 更新Mac部署

[Document] 更新Mac部署
- FILE: README.md; README_en.md
- ADD: OPENMP; MPS

# 具体内容

以[chatglm-6b-int4](https://huggingface.co/THUDM/chatglm-6b-int4)量化模型为例，做如下配置：

- 安装libomp的步骤;
- 对量化后模型等配置gcc编译项；
- 量化后模型启用MPS的解释。

README.md

@@ -240,7 +240,7 @@ else:
     source_code, kernel_file)
 ```
 
-> !注意，如果你之前运行过失败过，最好清一下Huggingface对缓存，i.e. `rm -rf ${HOME}/.cache/huggingface/modules/transformers_modules/chatglm-6b-int4`。由于使用了`rm`命令，请明确知道自己在删除什么。
+> 注意：如果你之前运行过失败过，最好清一下Huggingface的缓存，i.e. `rm -rf ${HOME}/.cache/huggingface/modules/transformers_modules/chatglm-6b-int4`。由于使用了`rm`命令，请明确知道自己在删除什么。
 
 ### Mac 上的 GPU 加速
 对于搭载了Apple Silicon的Mac（以及MacBook），可以使用 MPS 后端来在 GPU 上运行 ChatGLM-6B。需要参考 Apple 的 [官方说明](https://developer.apple.com/metal/pytorch) 安装 PyTorch-Nightly。
@@ -254,7 +254,7 @@ model = AutoModel.from_pretrained("your local path", trust_remote_code=True).hal
 model = AutoModel.from_pretrained("your local path", trust_remote_code=True).float().to('mps')
 ```
 
-> 注意上述方法在非量化版中，运行没有问题。量化版模型在MPS设备运行可以关注[这个](https://github.com/THUDM/ChatGLM-6B/issues/462)ISSUE，这主要是[kernel](https://huggingface.co/THUDM/chatglm-6b/blob/658202d88ac4bb782b99e99ac3adff58b4d0b813/quantization.py#L27)的原因，可以解包这个`ELF`文件看到是CUDA的实现。
+> 注意：上述方法在非量化版中，运行没有问题。量化版模型在MPS设备运行可以关注[这个](https://github.com/THUDM/ChatGLM-6B/issues/462)ISSUE，这主要是[kernel](https://huggingface.co/THUDM/chatglm-6b/blob/658202d88ac4bb782b99e99ac3adff58b4d0b813/quantization.py#L27)的原因，可以解包这个`ELF`文件看到是CUDA的实现。
 
 ### 多卡部署
 如果你有多张 GPU，但是每张 GPU 的显存大小都不足以容纳完整的模型，那么可以将模型切分在多张GPU上。首先安装 accelerate: `pip install accelerate`，然后通过如下方法加载模型：

README_en.md

@@ -188,15 +188,76 @@ 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:
+
+```sh
+clang: error: unsupported option '-fopenmp'
+clang: error: unsupported option '-fopenmp'
+```
+
+Take the quantified int4 version [chatglm-6b-int4](https://huggingface.co/THUDM/chatglm-6b-int4) for example, the following extra steps are needed:
+
+1. Install `libomp`;
+2. Configure `gcc`.
+
+```bash
+# STEP 1: install libopenmp, check `https://mac.r-project.org/openmp/` for details
+## Assumption: `gcc -v >= 14.x`, read the R-Poject before run the code:
+curl -O https://mac.r-project.org/openmp/openmp-14.0.6-darwin20-Release.tar.gz
+sudo tar fvxz openmp-14.0.6-darwin20-Release.tar.gz -C /
+## Four files are installed:
+#   usr/local/lib/libomp.dylib
+#   usr/local/include/ompt.h
+#   usr/local/include/omp.h
+#   usr/local/include/omp-tools.h
+```
+
+For `chatglm-6b-int4`, modify the [quantization.py](https://huggingface.co/THUDM/chatglm-6b-int4/blob/main/quantization.py)file. In the file, change the `gcc -O3 -fPIC -pthread -fopenmp -std=c99` configuration to `gcc -O3 -fPIC -Xclang -fopenmp -pthread  -lomp -std=c99`，[corresponding python code](https://huggingface.co/THUDM/chatglm-6b-int4/blob/63d66b0572d11cedd5574b38da720299599539b3/quantization.py#L168), i.e.:
+
+```python
+# STEP
+## Change the line contains `gcc -O3 -fPIC -pthread -fopenmp -std=c99` to:
+compile_command = "gcc -O3 -fPIC -Xclang -fopenmp -pthread  -lomp -std=c99 {} -shared -o {}".format(source_code, kernel_file)
+```
+
+For production code, one could use `platform` library to make it neater:
+
+```python
+## import platform just after `import os`
+import platform
+## ...
+## change the corresponding lines to:
+if platform.uname()[0] == 'Darwin':
+    compile_command = "gcc -O3 -fPIC -Xclang -fopenmp -pthread  -lomp -std=c99-o {}".format(
+    source_code, kernel_file)
+else:
+    compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(
+    source_code, kernel_file)
+```
+
+> Notice: If you have run 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.
 
 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 >= 13.3, one may encounter errors related to `half()` method. Use `float()` instead:
+
+```python
+model = AutoModel.from_pretrained("your local path", trust_remote_code=True).float().to('mps')
+```
+
 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`). Unzip/unpack [kernel](https://huggingface.co/THUDM/chatglm-6b/blob/658202d88ac4bb782b99e99ac3adff58b4d0b813/quantization.py#L27) as an `ELF` file shows its backend is `cuda`.
+
 ### 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.