Add Docker image for CUDA-12.1.

README.md

@@ -1049,6 +1049,7 @@ To simplify the deployment process, we provide docker images with pre-built envi
 1. Install the correct version of Nvidia driver depending on the image to use:
   - `qwenllm/qwen:cu117` (**recommend**): `>= 515.48.07`
   - `qwenllm/qwen:cu114` (w/o flash-attention): `>= 470.82.01`
+  - `qwenllm/qwen:cu121`: `>= 530.30.02`
   - `qwenllm/qwen:latest`: same as `qwenllm/qwen:cu117`
 
 2. Install and configure [docker](https://docs.docker.com/engine/install/) and [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html):

README_CN.md

@@ -1040,6 +1040,7 @@ print(response.choices[0].message.content)
 1. 根据需要使用的镜像版本，安装相应版本的Nvidia驱动：
   - `qwenllm/qwen:cu117`（**推荐**）：`>= 515.48.07`
   - `qwenllm/qwen:cu114`（不支持flash-attention）：`>= 470.82.01`
+  - `qwenllm/qwen:cu121`：`>= 530.30.02`
   - `qwenllm/qwen:latest`：与`qwenllm/qwen:cu117`相同
 
 2. 安装并配置[docker](https://docs.docker.com/engine/install/)和[nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)：

README_ES.md

@@ -909,6 +909,7 @@ Para simplificar el proceso de despliegue, proporcionamos imágenes Docker con e
 1. Instale la versión correcta del controlador Nvidia en función de la imagen que vaya a utilizar:
   - `qwenllm/qwen:cu117` (**recomendado**): `>= 515.48.07`
   - `qwenllm/qwen:cu114` (w/o flash-attention): `>= 470.82.01`
+  - `qwenllm/qwen:cu121`: `>= 530.30.02`
   - `qwenllm/qwen:latest`: igual que `qwenllm/qwen:cu117`
 
 2. Instale y configure [docker](https://docs.docker.com/engine/install/) y [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html):

README_FR.md

@@ -912,6 +912,7 @@ Pour simplifier le processus de déploiement, nous fournissons des images docker
 1. Installez la version correcte du pilote Nvidia en fonction de l'image à utiliser :
   - `qwenllm/qwen:cu117` (**recommandé**): `>= 515.48.07`
   - `qwenllm/qwen:cu114` (w/o flash-attention): `>= 470.82.01`
+  - `qwenllm/qwen:cu121`: `>= 530.30.02`
   - `qwenllm/qwen:latest`: même que `qwenllm/qwen:cu117`
 
 2. Installer et configurer [docker](https://docs.docker.com/engine/install/) et [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) :

README_JA.md

@@ -948,6 +948,7 @@ print(response.choices[0].message.content)
 1. 使用するイメージに応じて、正しいバージョンのNvidiaドライバをインストールしてください：
   - `qwenllm/qwen:cu117` (**recommend**): `>= 515.48.07`
   - `qwenllm/qwen:cu114` (w/o flash-attention): `>= 470.82.01`
+  - `qwenllm/qwen:cu121`: `>= 530.30.02`
   - `qwenllm/qwen:latest`: same as `qwenllm/qwen:cu117`
 
 2. [Docker](https://docs.docker.com/engine/install/) と [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) をインストールして設定します：

docker/Dockerfile-cu121

@@ -0,0 +1,122 @@
+ARG CUDA_VERSION=12.1.0
+ARG from=nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu20.04
+
+FROM ${from} as base
+
+ARG from
+
+RUN <<EOF
+apt update -y && apt upgrade -y && apt install -y --no-install-recommends  \
+    git \
+    git-lfs \
+    python3 \
+    python3-pip \
+    python3-dev \
+    wget \
+    vim \
+&& rm -rf /var/lib/apt/lists/*
+EOF
+
+RUN ln -s /usr/bin/python3 /usr/bin/python
+
+RUN git lfs install
+
+FROM base as dev
+
+WORKDIR /
+
+RUN mkdir -p /data/shared/Qwen
+
+WORKDIR /data/shared/Qwen/
+
+# Users can also mount '/data/shared/Qwen/' to keep the data
+COPY ../requirements.txt ./
+COPY ../requirements_web_demo.txt ./
+
+FROM dev as bundle_req
+
+ARG BUNDLE_REQUIREMENTS=true
+
+RUN <<EOF
+if [ "$BUNDLE_REQUIREMENTS" = "true" ]; then 
+    cd /data/shared/Qwen
+    pip3 install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121
+    pip3 install -r requirements.txt
+    pip3 install -r requirements_web_demo.txt
+
+    pip3 install transformers==4.36.0
+fi
+EOF
+
+FROM bundle_req as bundle_flash_attention
+ARG BUNDLE_FLASH_ATTENTION=true
+
+RUN <<EOF 
+if [ "$BUNDLE_FLASH_ATTENTION" = "true" ]; then
+    cd /data/shared/Qwen 
+    test -d flash-attention || git clone -b v2.3.3 https://github.com/Dao-AILab/flash-attention
+    cd /data/shared/Qwen/flash-attention &&
+        pip3 install . &&
+        pip3 install csrc/layer_norm
+fi
+EOF
+
+FROM bundle_flash_attention as bundle_finetune
+ARG BUNDLE_FINETUNE=true
+
+RUN <<EOF
+if [ "$BUNDLE_FINETUNE" = "true" ]; then
+    cd /data/shared/Qwen
+
+    # Full-finetune / LoRA.
+    pip3 install "deepspeed==0.12.6" "peft==0.7.1"
+
+    # Q-LoRA.
+    apt update -y && DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends \
+        libopenmpi-dev openmpi-bin \
+        && rm -rf /var/lib/apt/lists/*
+    pip3 install "optimum==1.14.0" "auto-gptq==0.5.0" mpi4py
+fi
+EOF
+
+FROM bundle_finetune as bundle_openai_api
+ARG BUNDLE_OPENAI_API=true
+
+RUN <<EOF
+if [ "$BUNDLE_OPENAI_API" = "true" ]; then
+    cd /data/shared/Qwen
+
+    pip3 install fastapi uvicorn "openai<1.0.0" sse_starlette "pydantic<=1.10.13"
+fi
+EOF
+
+FROM bundle_openai_api as bundle_vllm
+ARG BUNDLE_VLLM=true
+
+RUN <<EOF
+if [ "$BUNDLE_VLLM" = "true" ]; then
+    cd /data/shared/Qwen
+
+    pip3 install vllm==0.2.7 "fschat[model_worker,webui]==0.2.34"
+fi
+EOF
+
+FROM bundle_vllm as final
+ARG from
+
+COPY ../requirements.txt ./
+COPY ../requirements_web_demo.txt ./
+COPY ../cli_demo.py ./
+COPY ../web_demo.py ./
+COPY ../openai_api.py ./
+COPY ../finetune.py ./
+COPY ../utils.py ./
+COPY ./examples/* ./examples/
+COPY ./eval/* ./eval/
+COPY ./finetune/* ./finetune/
+
+EXPOSE 80
+
+WORKDIR /data/shared/Qwen/
+
+CMD ["python3", "web_demo.py", "--server-port", "80", "--server-name", "0.0.0.0", "-c", "/data/shared/Qwen/Qwen-Chat/"]

finetune.py

@@ -272,7 +272,7 @@ def train():
 
     local_rank = training_args.local_rank
 
-    device_map = "auto"
+    device_map = None
     world_size = int(os.environ.get("WORLD_SIZE", 1))
     ddp = world_size != 1
     if lora_args.q_lora:
@@ -282,6 +282,19 @@ def train():
                 "FSDP or ZeRO3 are incompatible with QLoRA."
             )
 
+    is_chat_model = 'chat' in model_args.model_name_or_path.lower()
+    if (
+            training_args.use_lora
+            and not lora_args.q_lora
+            and deepspeed.is_deepspeed_zero3_enabled()
+            and not is_chat_model
+    ):
+        raise RuntimeError("ZeRO3 is incompatible with LoRA when finetuning on base model.")
+
+    model_load_kwargs = {}
+    if deepspeed.is_deepspeed_zero3_enabled():
+        model_load_kwargs['low_cpu_mem_usage'] = False
+
     # Set RoPE scaling factor
     config = transformers.AutoConfig.from_pretrained(
         model_args.model_name_or_path,
@@ -302,6 +315,7 @@ def train():
         )
         if training_args.use_lora and lora_args.q_lora
         else None,
+        **model_load_kwargs,
     )
     tokenizer = transformers.AutoTokenizer.from_pretrained(
         model_args.model_name_or_path,
@@ -314,7 +328,7 @@ def train():
     tokenizer.pad_token_id = tokenizer.eod_id
 
     if training_args.use_lora:
-        if lora_args.q_lora or 'chat' in model_args.model_name_or_path.lower():
+        if lora_args.q_lora or is_chat_model:
             modules_to_save = None
         else:
             modules_to_save = ["wte", "lm_head"]

finetune/finetune_ds.sh

@@ -18,7 +18,7 @@ NNODES=${NNODES:-1}
 NODE_RANK=${NODE_RANK:-0}
 
 # The ip address of the rank-0 worker, for single-worker training, please set to localhost
-MASTER_ADDR=${MASTER_ADDR:localhost}
+MASTER_ADDR=${MASTER_ADDR:-localhost}
 
 # The port for communication
 MASTER_PORT=${MASTER_PORT:-6001}

finetune/finetune_lora_ds.sh

@@ -18,7 +18,7 @@ NNODES=${NNODES:-1}
 NODE_RANK=${NODE_RANK:-0}
 
 # The ip address of the rank-0 worker, for single-worker training, please set to localhost
-MASTER_ADDR=${MASTER_ADDR:localhost}
+MASTER_ADDR=${MASTER_ADDR:-localhost}
 
 # The port for communication
 MASTER_PORT=${MASTER_PORT:-6001}

finetune/finetune_qlora_ds.sh

@@ -18,7 +18,7 @@ NNODES=${NNODES:-1}
 NODE_RANK=${NODE_RANK:-0}
 
 # The ip address of the rank-0 worker, for single-worker training, please set to localhost
-MASTER_ADDR=${MASTER_ADDR:localhost}
+MASTER_ADDR=${MASTER_ADDR:-localhost}
 
 # The port for communication
 MASTER_PORT=${MASTER_PORT:-6001}