DeepSeek V3/R1/Prover-V2 671B SFT with LoRA

Author: pbelevich

3.test_cases/pytorch/colossalai/README.md

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+# Colossal-AI
+
+## Dependencies
+
+As of Apr 18th 2025 [commit](https://github.com/hpcaitech/ColossalAI/tree/46ed5d856b16b074325091a88e761544b3d4f9f0) ColosalAI required PyTorch 2.5.1 which official builds use CUDA 12.4. We use `nvidia/cuda:12.4.1-devel-ubuntu22.04` as the base image and install all dependencies on top of it in [colossalai.Dockerfile](colossalai.Dockerfile).
+
+## Build Docker Image
+
+Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing docker build. We launch the build job on the GPU node:
+
+Login to AWS ECR:
+```bash
+export AWS_ACCESS_KEY_ID=...
+export AWS_SECRET_ACCESS_KEY=...
+
+aws ecr get-login-password ...
+```
+
+Build the docker image on the GPU node and push it to the docker repo:
+```bash
+export DOCKER_REPO=159553542841.dkr.ecr.ap-northeast-1.amazonaws.com/belevich/colossalai
+srun ./build_docker.sh
+```
+
+Take docker image from the docker repo:
+```bash
+docker pull $DOCKER_REPO:latest
+```
+
+Import the docker image to an enroot container(maybe remove previous created `rm ./colossalai.sqsh`):
+```bash
+enroot import -o ./colossalai.sqsh  dockerd://$DOCKER_REPO:latest
+```
+
+

3.test_cases/pytorch/colossalai/build_docker.sh

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+#! /bin/bash
+
+if [ -z "$SLURM_JOB_ID" ]; then
+    echo "Run with slurm: srun ./build_docker.sh"
+    exit 1
+fi
+
+docker build --progress=plain -f colossalai.Dockerfile -t colossalai:latest .
+
+if [ $? -ne 0 ]; then
+    echo "Failed to build docker image"
+    exit 1
+fi
+
+if [ -z "$DOCKER_REPO" ]; then
+    echo "DOCKER_REPO is not set"
+    exit 1
+fi
+
+docker tag colossalai:latest $DOCKER_REPO:latest
+
+docker push $DOCKER_REPO:latest

3.test_cases/pytorch/colossalai/colossalai.Dockerfile

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+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+FROM nvidia/cuda:12.4.1-devel-ubuntu22.04
+
+ARG GDRCOPY_VERSION=v2.4.4
+ARG EFA_INSTALLER_VERSION=1.38.1
+ARG AWS_OFI_NCCL_VERSION=v1.14.0
+ARG NCCL_VERSION=v2.26.2-1
+ARG NCCL_TESTS_VERSION=v2.14.1
+
+RUN apt-get update -y && apt-get upgrade -y
+RUN apt-get remove -y --allow-change-held-packages \
+    ibverbs-utils \
+    libibverbs-dev \
+    libibverbs1 \
+    libmlx5-1 \
+    libnccl2 \
+    libnccl-dev
+
+RUN rm -rf /opt/hpcx \
+    && rm -rf /usr/local/mpi \
+    && rm -f /etc/ld.so.conf.d/hpcx.conf \
+    && ldconfig
+
+ENV OPAL_PREFIX=
+
+RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
+    apt-utils \
+    autoconf \
+    automake \
+    build-essential \
+    check \
+    cmake \
+    curl \
+    debhelper \
+    devscripts \
+    git \
+    gcc \
+    gdb \
+    kmod \
+    libsubunit-dev \
+    libtool \
+    openssh-client \
+    openssh-server \
+    pkg-config \
+    python3-distutils \
+    vim
+RUN apt-get purge -y cuda-compat-*
+
+RUN mkdir -p /var/run/sshd
+RUN sed -i 's/[ #]\(.*StrictHostKeyChecking \).*/ \1no/g' /etc/ssh/ssh_config && \
+    echo "    UserKnownHostsFile /dev/null" >> /etc/ssh/ssh_config && \
+    sed -i 's/#\(StrictModes \).*/\1no/g' /etc/ssh/sshd_config
+
+ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:/opt/amazon/openmpi/lib:/opt/nccl/build/lib:/opt/amazon/efa/lib:/opt/aws-ofi-nccl/install/lib:/usr/local/lib:$LD_LIBRARY_PATH
+ENV PATH /opt/amazon/openmpi/bin/:/opt/amazon/efa/bin:/usr/bin:/usr/local/bin:$PATH
+
+RUN curl https://bootstrap.pypa.io/get-pip.py -o /tmp/get-pip.py \
+    && python3 /tmp/get-pip.py \
+    && pip3 install awscli pynvml
+
+#################################################
+## Install NVIDIA GDRCopy
+##
+## NOTE: if `nccl-tests` or `/opt/gdrcopy/bin/sanity -v` crashes with incompatible version, ensure
+## that the cuda-compat-xx-x package is the latest.
+RUN git clone -b ${GDRCOPY_VERSION} https://github.com/NVIDIA/gdrcopy.git /tmp/gdrcopy \
+    && cd /tmp/gdrcopy \
+    && make prefix=/opt/gdrcopy install
+
+ENV LD_LIBRARY_PATH /opt/gdrcopy/lib:$LD_LIBRARY_PATH
+ENV LIBRARY_PATH /opt/gdrcopy/lib:$LIBRARY_PATH
+ENV CPATH /opt/gdrcopy/include:$CPATH
+ENV PATH /opt/gdrcopy/bin:$PATH
+
+#################################################
+## Install EFA installer
+RUN cd $HOME \
+    && curl -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz \
+    && tar -xf $HOME/aws-efa-installer-${EFA_INSTALLER_VERSION}.tar.gz \
+    && cd aws-efa-installer \
+    && ./efa_installer.sh -y -g -d --skip-kmod --skip-limit-conf --no-verify \
+    && rm -rf $HOME/aws-efa-installer
+
+###################################################
+## Install NCCL
+RUN git clone -b ${NCCL_VERSION} https://github.com/NVIDIA/nccl.git  /opt/nccl \
+    && cd /opt/nccl \
+    && make -j $(nproc) src.build CUDA_HOME=/usr/local/cuda \
+    NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_89,code=sm_89 -gencode=arch=compute_90,code=sm_90"
+
+###################################################
+## Install AWS-OFI-NCCL plugin
+RUN DEBIAN_FRONTEND=noninteractive apt-get install -y libhwloc-dev
+#Switch from sh to bash to allow parameter expansion
+SHELL ["/bin/bash", "-c"]
+RUN curl -OL https://github.com/aws/aws-ofi-nccl/releases/download/${AWS_OFI_NCCL_VERSION}/aws-ofi-nccl-${AWS_OFI_NCCL_VERSION//v}.tar.gz \
+    && tar -xf aws-ofi-nccl-${AWS_OFI_NCCL_VERSION//v}.tar.gz \
+    && cd aws-ofi-nccl-${AWS_OFI_NCCL_VERSION//v} \
+    && ./configure --prefix=/opt/aws-ofi-nccl/install \
+        --with-mpi=/opt/amazon/openmpi \
+        --with-libfabric=/opt/amazon/efa \
+        --with-cuda=/usr/local/cuda \
+        --enable-platform-aws \
+    && make -j $(nproc) \
+    && make install \
+    && cd .. \
+    && rm -rf aws-ofi-nccl-${AWS_OFI_NCCL_VERSION//v} \
+    && rm aws-ofi-nccl-${AWS_OFI_NCCL_VERSION//v}.tar.gz
+
+SHELL ["/bin/sh", "-c"]
+
+###################################################
+## Install NCCL-tests
+RUN git clone -b ${NCCL_TESTS_VERSION} https://github.com/NVIDIA/nccl-tests.git /opt/nccl-tests \
+    && cd /opt/nccl-tests \
+    && make -j $(nproc) \
+    MPI=1 \
+    MPI_HOME=/opt/amazon/openmpi/ \
+    CUDA_HOME=/usr/local/cuda \
+    NCCL_HOME=/opt/nccl/build \
+    NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_89,code=sm_89 -gencode=arch=compute_90,code=sm_90"
+
+RUN rm -rf /var/lib/apt/lists/*
+
+## Set Open MPI variables to exclude network interface and conduit.
+ENV OMPI_MCA_pml=^ucx            \
+    OMPI_MCA_btl=tcp,self           \
+    OMPI_MCA_btl_tcp_if_exclude=lo,docker0,veth_def_agent\
+    OPAL_PREFIX=/opt/amazon/openmpi \
+    NCCL_SOCKET_IFNAME=^docker,lo,veth
+
+## Turn off PMIx Error https://github.com/open-mpi/ompi/issues/7516
+ENV PMIX_MCA_gds=hash
+
+## Set LD_PRELOAD for NCCL library
+ENV LD_PRELOAD /opt/nccl/build/lib/libnccl.so
+
+# Install Miniconda to not depend on the base image python
+RUN mkdir -p /opt/miniconda3 \
+    && curl -L https://repo.anaconda.com/miniconda/Miniconda3-py312_25.3.1-1-Linux-x86_64.sh -o /tmp/Miniconda3-py312_25.3.1-1-Linux-x86_64.sh \
+    && bash /tmp/Miniconda3-py312_25.3.1-1-Linux-x86_64.sh -b -f -p /opt/miniconda3 \
+    && rm /tmp/Miniconda3-py312_25.3.1-1-Linux-x86_64.sh \
+    && /opt/miniconda3/bin/conda init bash
+
+ENV PATH="/opt/miniconda3/bin:${PATH}"
+
+COPY gather_state_dict_fast.patch /tmp/gather_state_dict_fast.patch
+
+RUN git clone https://github.com/hpcaitech/ColossalAI.git /tmp/colossalai && \
+    cd /tmp/colossalai && \
+    git checkout 46ed5d856b16b074325091a88e761544b3d4f9f0 && \
+    git apply /tmp/gather_state_dict_fast.patch && \
+    # BUILD_EXT=1 FORCE_CUDA=1 
+    pip install . && \
+    cd applications/ColossalChat && \
+    pip install .
+
+ENV TORCH_CUDA_ARCH_LIST="9.0a"
+
+# because of  https://discuss.huggingface.co/t/valueerror-unable-to-avoid-copy-while-creating-an-array-as-requested/93584/5
+RUN pip install "numpy<2.0"
+
+# Install tensornvme from github because pipy version is totallyoutdated
+RUN apt update -y && apt install -y libaio-dev && pip install -v git+https://github.com/hpcaitech/TensorNVMe.git
+
+# To use the fused RMSNorm kernel colossalai needs apex built from source:
+RUN git clone https://github.com/NVIDIA/apex /tmp/apex && \
+    cd /tmp/apex && \
+    NVCC_APPEND_FLAGS="--threads 4" \
+    pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext --cuda_ext --parallel 8" ./
+
+# Build flash-attn
+RUN MAX_JOBS=48 pip install flash-attn==2.7.4.post1 --no-cache-dir --no-deps --no-build-isolation --verbose --force-reinstall
+
+# Install transformers==4.52.4 for better support of DeepSeek
+RUN pip install transformers==4.52.4
+
+RUN pip install math-verify==0.7.0 tqdm

3.test_cases/pytorch/colossalai/deepseek-lora-finetune/.gitignore

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+DeepSeek-V3
+logs

3.test_cases/pytorch/colossalai/deepseek-lora-finetune/README.md

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+# DeepSeek V3/R1/Prover-V2 671B SFT with LoRA
+
+This example uses Colossal-AI container from the parent directory
+
+## Download model weights
+
+```bash
+pip install -U "huggingface_hub[cli]"
+```
+
+Choose the model you want to finetune:
+
+ - deepseek-ai/DeepSeek-V3
+ - deepseek-ai/DeepSeek-V3-0324
+ - deepseek-ai/DeepSeek-R1
+ - deepseek-ai/DeepSeek-Prover-V2-671B
+
+and define model name environment variable, for example:
+```bash
+export MODEL_NAME="deepseek-ai/DeepSeek-R1"
+```
+
+Download the model weights from Hugging Face and find the model path:
+```bash
+huggingface-cli download $MODEL_NAME
+export MODEL_PATH=`python -c "from pathlib import Path; from huggingface_hub import hf_hub_download; print(Path(hf_hub_download('$MODEL_NAME', filename='config.json')).parent)"`
+export HF_HOME=${HF_HOME:-$(python -c "from pathlib import Path; from huggingface_hub import hf_hub_download; print(Path(hf_hub_download('$MODEL_NAME', filename='config.json')).parent.parent.parent.parent.parent)")}
+```
+
+## Convert fp8 weights to bf16
+
+Since the model weights are fp8 and SFT requires bf16 weights, we use `convert_to_bf16.py` from `DeepSeek-V3` repo to convert the weights to bf16:
+
+Clone DeepSeek V3 repo:
+```bash
+git clone https://github.com/deepseek-ai/DeepSeek-V3.git
+```
+Launch a job on the GPU node:
+```bash
+srun \
+    --container-image ../colossalai.sqsh \
+    --container-mounts ./:/workdir,$HF_HOME:$HF_HOME \
+    python /workdir/DeepSeek-V3/inference/fp8_cast_bf16.py \
+        --input-fp8-hf-path $MODEL_PATH \
+        --output-bf16-hf-path /workdir/$MODEL_NAME-bf16
+```
+
+Copy the model config and tokenizer files to the output directory:
+```bash
+cp -L $MODEL_PATH/*.json ./$MODEL_NAME-bf16/
+cp -L $MODEL_PATH/*.py ./$MODEL_NAME-bf16/
+```
+
+## Launch LoRA finetuning
+
+```bash
+sbatch lora_finetune.sbatch $MODEL_NAME AI-MO/NuminaMath-TIR train
+```
+Check the logs:
+```bash
+tail -f -n +0 slurm-XXX.out
+```
+Example output on 15 p5en nodes(DP5, PP3, EP8):
+```
+Step:   3%|▎         | 6/224 [22:39<5:48:26, 95.90s/it, loss=00.794, grad_norm=0.161]
+Step:   5%|▌         | 12/224 [25:24<2:14:47, 37.97s/it, loss=0.506, grad_norm=0.108]
+Step:   8%|▊         | 17/224 [28:09<1:38:50, 28.65s/it, loss=0.442, grad_norm=0.124]
+Step:  10%|█         | 23/224 [30:54<1:32:21, 27.57s/it, loss=0.429, grad_norm=0.0904]
+Step:  13%|█▎        | 29/224 [33:16<1:34:26, 29.06s/it, loss=0.411, grad_norm=0.0404]
+Step:  16%|█▌        | 34/224 [36:55<1:43:15, 32.61s/it, loss=0.383, grad_norm=0.0298]
+Step:  18%|█▊        | 40/224 [40:09<1:33:32, 30.50s/it, loss=0.368, grad_norm=0.0255]
+Step:  21%|██        | 46/224 [42:27<1:22:43, 27.89s/it, loss=0.367, grad_norm=0.0252]
+Step:  23%|██▎       | 51/224 [45:13<1:19:52, 27.70s/it, loss=0.354, grad_norm=0.0262]
+Step:  25%|██▌       | 57/224 [47:31<1:16:52, 27.62s/it, loss=0.346, grad_norm=0.0232]
+Step:  28%|██▊       | 62/224 [50:16<1:14:09, 27.47s/it, loss=0.355, grad_norm=0.0211]
+Step:  30%|███       | 68/224 [52:34<1:11:46, 27.61s/it, loss=0.336, grad_norm=0.0214]
+Step:  33%|███▎      | 73/224 [55:36<1:14:31, 29.61s/it, loss=0.34, ggrad_norm=0.021]
+Step:  35%|███▌      | 79/224 [57:57<1:07:50, 28.01s/it, loss=0.339, grad_norm=0.0212]
+Step:  38%|███▊      | 84/224 [1:00:27<1:13:01, 31.30s/it, loss=0.325, grad_norm=0.0224]
+Step:  40%|███▉      | 89/224 [1:03:35<1:07:18, 29.92s/it, loss=0.324, grad_norm=0.0206]
+Step:  42%|████▏     | 95/224 [1:05:52<1:00:10, 27.78s/it, loss=0.338, grad_norm=0.0224]
+Step:  45%|████▍     | 100/224 [1:08:08<56:34, 27.37s/it, loss=0.325, grad_norm=0.0213]
+Step:  47%|████▋     | 105/224 [1:10:53<54:21, 27.41s/it, loss=0.318, grad_norm=0.0206]
+Step:  49%|████▉     | 110/224 [1:13:11<51:55, 27.33s/it, loss=0.342, grad_norm=0.0208]
+Step:  52%|█████▏    | 116/224 [1:15:40<51:53, 28.56s/it, loss=0.334, grad_norm=0.0214]
+Step:  54%|█████▍    | 121/224 [1:18:04<48:21, 28.62s/it, loss=0.336, grad_norm=0.02]
+Step:  56%|█████▋    | 126/224 [1:20:21<44:45, 27.40s/it, loss=0.33, grad_norm=0.0211]
+Step:  58%|█████▊    | 131/224 [1:22:38<42:28, 27.41s/it, loss=0.326, grad_norm=0.022]
+Step:  61%|██████    | 136/224 [1:25:29<46:47, 31.90s/it, loss=0.344, grad_norm=0.0233]
+Step:  63%|██████▎   | 141/224 [1:28:45<38:47, 28.05s/it, loss=0.328, grad_norm=0.0218]
+Step:  65%|██████▌   | 146/224 [1:30:29<35:42, 27.47s/it, loss=0.329, grad_norm=0.0218]
+Step:  67%|██████▋   | 151/224 [1:32:47<33:11, 27.28s/it, loss=0.33, grad_norm=0.0208]
+Step:  70%|██████▉   | 156/224 [1:35:16<32:35, 28.75s/it, loss=0.322, grad_norm=0.0216]
+Step:  72%|███████▏  | 161/224 [1:37:37<30:45, 29.29s/it, loss=0.328, grad_norm=0.0238]
+Step:  74%|███████▍  | 166/224 [1:39:26<26:40, 27.60s/it, loss=0.313, grad_norm=0.0236]
+Step:  76%|███████▋  | 171/224 [1:41:43<24:12, 27.40s/it, loss=0.337, grad_norm=0.0435]
+Step:  79%|███████▊  | 176/224 [1:44:00<21:54, 27.39s/it, loss=0.328, grad_norm=0.0222]
+Step:  81%|████████  | 181/224 [1:46:24<21:01, 29.35s/it, loss=0.332, grad_norm=0.0226]
+Step:  83%|████████▎ | 186/224 [1:49:02<18:43, 29.57s/it, loss=0.329, grad_norm=0.0215]
+Step:  85%|████████▌ | 191/224 [1:51:18<15:45, 27.81s/it, loss=0.325, grad_norm=0.0217]
+Step:  88%|████████▋ | 195/224 [1:53:42<14:02, 29.06s/it, loss=0.331, grad_norm=0.0221]
+Step:  89%|████████▉ | 200/224 [1:55:59<11:04, 27.69s/it, loss=0.32, grad_norm=0.0207]
+Step:  92%|█████████▏| 205/224 [1:58:00<09:17, 29.32s/it, loss=0.311, grad_norm=0.0224]
+Step:  94%|█████████▍| 210/224 [2:00:16<06:27, 27.64s/it, loss=0.327, grad_norm=0.023]
+Step:  96%|█████████▌| 214/224 [2:02:34<04:35, 27.57s/it, loss=0.315, grad_norm=0.0273]
+Step:  98%|█████████▊| 219/224 [2:04:50<02:16, 27.38s/it, loss=0.348, grad_norm=0.0217]
+Step: 100%|██████████| 224/224 [2:06:39<00:00, 27.30s/it, loss=0.325, grad_norm=0.0236]
+
+Start saving final model checkpoint to /workdir/deepseek-ai/DeepSeek-R1-bf16-lora
+Saved final model checkpoint at epoch 0 at folder /workdir/deepseek-ai/DeepSeek-R1-bf16-lora in 63.06 seconds
+
+```
+
+## Launch LoRA evaluation
+
+```bash
+srun \
+    --mpi=pmix --cpu-bind=none \
+    --container-image ../colossalai.sqsh \
+    --container-mounts ./:/workdir,$HF_HOME:$HF_HOME \
+    python /workdir/lora_eval.py -m deepseek-ai/DeepSeek-R1 -d AI-MO/NuminaMath-TIR
+```