mirror of https://github.com/THUDM/ChatGLM-6B
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README.md
60
README.md
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@ -191,14 +191,70 @@ model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True
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如果遇到了报错 `Could not find module 'nvcuda.dll'` 或者 `RuntimeError: Unknown platform: darwin` (MacOS) ，请[从本地加载模型](README.md#从本地加载模型)
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### Mac 上的 CPU 部署和加速
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Mac直接加载量化后的模型会出现问题（可运行但是单核），这是由于Mac由于本身缺乏omp导致的。
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```sh
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clang: error: unsupported option '-fopenmp'
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clang: error: unsupported option '-fopenmp'
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```
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以[chatglm-6b-int4](https://huggingface.co/THUDM/chatglm-6b-int4)量化模型为例，需要做如下配置：
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1. 安装`libomp`;
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2. 配置`gcc`编译项。
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```bash
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# 第一步: 参考`https://mac.r-project.org/openmp/`
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## 假设gcc -v是14.x版本，其他版本见R-Project提供的表格
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curl -O https://mac.r-project.org/openmp/openmp-14.0.6-darwin20-Release.tar.gz
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sudo tar fvxz openmp-14.0.6-darwin20-Release.tar.gz -C /
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## 此时会安装下面几个文件：
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# usr/local/lib/libomp.dylib
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# usr/local/include/ompt.h
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# usr/local/include/omp.h
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# usr/local/include/omp-tools.h
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```
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针对`chatglm-6b-int4`, 修改[quantization.py](https://huggingface.co/THUDM/chatglm-6b-int4/blob/main/quantization.py)，主要是把硬编码的`gcc -O3 -fPIC -pthread -fopenmp -std=c99`命令修改成`gcc -O3 -fPIC -Xclang -fopenmp -pthread -lomp -std=c99`，[对应代码](https://huggingface.co/THUDM/chatglm-6b-int4/blob/63d66b0572d11cedd5574b38da720299599539b3/quantization.py#L168)见下:
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```python
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# 第二步
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## 找到包含`gcc -O3 -fPIC -pthread -fopenmp -std=c99`的这一行
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## 修改成
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compile_command = "gcc -O3 -fPIC -Xclang -fopenmp -pthread -lomp -std=c99 {} -shared -o {}".format(source_code, kernel_file)
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```
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为了兼容性，也能写成
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```python
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## 在最开始增加一个包
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import platform
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## ...
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## 上述相应部分修改为（请自行改一下缩进）：
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if platform.uname()[0] == 'Darwin':
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compile_command = "gcc -O3 -fPIC -Xclang -fopenmp -pthread -lomp -std=c99-o {}".format(
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source_code, kernel_file)
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else:
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compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(
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source_code, kernel_file)
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```
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> 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。
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### Mac 上的 GPU 加速
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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。
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目前在 MacOS 上只支持[从本地加载模型](README.md#从本地加载模型)。将代码中的模型加载改为从本地加载，并使用 mps 后端
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目前在 MacOS 上只支持[从本地加载模型](README.md#从本地加载模型)。将代码中的模型加载改为从本地加载，并使用 mps 后端：
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```python
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model = AutoModel.from_pretrained("your local path", trust_remote_code=True).half().to('mps')
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```
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即可使用在 Mac 上使用 GPU 加速模型推理。
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即可使用在 Mac 上使用 GPU 加速模型推理。如果出现关于`half`的报错（比如在MacOS 13.3.x上），可以改成：
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```python
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model = AutoModel.from_pretrained("your local path", trust_remote_code=True).float().to('mps')
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```
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> 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。
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### 多卡部署
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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：
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61
README_en.md
61
README_en.md
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@ -188,6 +188,58 @@ model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=Tru
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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).
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### CPU Deployment on Mac
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The default Mac enviroment does not support Openmp. One may encounter such warning/errors when execute the `AutoModel.from_pretrained(...)` command:
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```sh
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clang: error: unsupported option '-fopenmp'
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clang: error: unsupported option '-fopenmp'
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```
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Take the quantified int4 version [chatglm-6b-int4](https://huggingface.co/THUDM/chatglm-6b-int4) for example, the following extra steps are needed:
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1. Install `libomp`;
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2. Configure `gcc`.
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```bash
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# STEP 1: install libopenmp, check `https://mac.r-project.org/openmp/` for details
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## Assumption: `gcc -v >= 14.x`, read the R-Poject before run the code:
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curl -O https://mac.r-project.org/openmp/openmp-14.0.6-darwin20-Release.tar.gz
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sudo tar fvxz openmp-14.0.6-darwin20-Release.tar.gz -C /
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## Four files are installed:
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# usr/local/lib/libomp.dylib
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# usr/local/include/ompt.h
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# usr/local/include/omp.h
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# usr/local/include/omp-tools.h
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```
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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.:
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```python
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# STEP
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## Change the line contains `gcc -O3 -fPIC -pthread -fopenmp -std=c99` to:
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compile_command = "gcc -O3 -fPIC -Xclang -fopenmp -pthread -lomp -std=c99 {} -shared -o {}".format(source_code, kernel_file)
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```
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For production code, one could use `platform` library to make it neater:
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```python
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## import platform just after `import os`
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import platform
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## ...
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## change the corresponding lines to:
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if platform.uname()[0] == 'Darwin':
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compile_command = "gcc -O3 -fPIC -Xclang -fopenmp -pthread -lomp -std=c99-o {}".format(
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source_code, kernel_file)
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else:
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compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(
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source_code, kernel_file)
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```
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> 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.
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### GPU Inference on Mac
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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.
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@ -195,8 +247,17 @@ Currently you must [load the model locally](README_en.md#load-the-model-locally)
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```python
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model = AutoModel.from_pretrained("your local path", trust_remote_code=True).half().to('mps')
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```
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For Mac users with Mac >= 13.3, one may encounter errors related to `half()` method. Use `float()` instead:
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```python
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model = AutoModel.from_pretrained("your local path", trust_remote_code=True).float().to('mps')
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```
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Then you can use GPU-accelerated model inference on Mac.
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> 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`.
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### Multi-GPU Deployment
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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.
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