Add README

README.md

+# 基于llama-factory启动的GRPO训练仓库
+
+## 环境配置
+### Docker（方法一）
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250724
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/llama-grpo
+pip install -e .
+pip uninstall trl
+cd ../
+git clone -b v0.19.0 https://github.com/huggingface/trl.git
+mv trl trl-v0.19.0
+cd trl-v0.19.0
+pip install -e .
+cd ../llama-grpo
+pip install transformers==4.51.3
+bash train.sh x
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t llama-grpo:latest .
+
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/llama-grpo
+pip install -e .
+pip uninstall trl
+cd ../
+git clone -b v0.19.0 https://github.com/huggingface/trl.git
+mv trl trl-v0.19.0
+cd trl-v0.19.0
+pip install -e .
+cd ../llama-grpo
+pip install transformers==4.51.3
+bash train.sh x
+```
+
+### Anaconda（方法三）
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+```bash
+DTK: 25.04.1
+python: 3.10.12
+torch: 2.4.1+das.opt1.dtk25041
+vllm: 0.8.5
+transformers: 4.51.3
+deepspeed: 0.14.2+das.opt1.dtk25041
+```
+
+## 训练方法
+### 多节点启动
+1. 启动`trl vllm-serve`
+```bash
+bash start_vllm_serve.sh
+```
+
+2. 启动训练
+需要在每个服务器分别启动下面脚本，第一台服务器x=0，第二台服务器x=1，依次类推
+其他参数请参考脚本内的参数对应说明进行配置修改
+```bash
+bash train.sh x
+```
+
+### slurm启动
+1. vllm-serve启动
+```bash
+sbatch sbatch_vllm.sh
+```
+
+2. 训练启动
+需要在`sbatch_train.sh`脚本内修改参数，并提交任务
+```bash
+sbatch sbatch_train.sh
+```
+
+
+
+## 已知问题
+如果出现`cannot re`
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data/dataset_info.json

 {
+  "dapo_math": {
+    "hf_hub_url": "open-r1/DAPO-Math-17k-Processed",
+    "split": "train",
+    "subset": "all",
+    "columns": {
+      "prompt": "prompt",
+      "response": "solution"
+    }
+  },
+  "OpenMathReasoning-mini": {
+    "hf_hub_url": "unsloth/OpenMathReasoning-mini",
+    "split": "train",
+    "columns": {
+      "prompt": "problem",
+      "response": "expected_answer"
+    }
+  },
+  "hiyouga-math12k": {
+    "hf_hub_url": "hiyouga/math12k",
+    "split": "train",
+    "columns": {
+      "prompt": "problem",
+      "response": "answer"
+    }
+  },
   "identity": {
     "file_name": "identity.json"
   },

docker/Dockerfile

-FROM docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250711
+FROM docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250724
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sbatch_train.sh

+#!/bin/bash
+#SBATCH --job-name=grpo_train           # 作业名称
+#SBATCH --output=logs/grpo_train_%j.out # 输出日志文件
+#SBATCH --error=logs/grpo_train_%j.out  # 错误日志文件
+#SBATCH --nodes=2                         # 使用节点数量
+#SBATCH --qos=dcudvp
+#SBATCH --gres=dcu:8                      # 每节点 8 张 DCU
+#SBATCH --cpus-per-task=32                # 每个任务分配 32 个 CPU
+#SBATCH --partition=dcu                   # 使用 DCU 分区sinfo
+#SBATCH --ntasks-per-node=1
+#SBATCH --mem=960G
+
+NODE_LIST=($(scontrol show hostnames $SLURM_JOB_NODELIST))
+
+for RANK in "${!NODE_LIST[@]}"; do
+    node="${NODE_LIST[$RANK]}"
+    srun --nodes=1 --exclusive -w $node bash <<EOF &
+
+source ~/packages/dtk-25.04.1/env.sh
+source ~/miniconda3/etc/profile.d/conda.sh
+conda activate grpo
+
+export DISABLE_VERSION_CHECK=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 # 默认节点8张卡
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export HF_ENDPOINT=https://hf-mirror.com
+
+export MASTER_ADDR=XXXXXX ## 实际启动mastet节点 hostname或者IP地址
+export MASTER_ADDR=${NODE_LIST[0]}
+export RANK=$RANK
+export MASTER_PORT=29568
+export WORLD_SIZE=$((8 * ${#NODE_LIST[@]}))
+
+export NCCL_SOCKET_IFNAME=ibxxxxx # ifconfig查看实际IB网口名
+export NCCL_DEBUG=INFO
+export NCCL_ALGO=Ring
+export NCCL_PROTO=Simple
+export NCCL_MIN_NCHANNELS=32
+export NCCL_MAX_NCHANNELS=32
+export NCCL_MIN_P2P_NCHANNELS=32
+export NCCL_MAX_P2P_NCHANNELS=32
+export NCCL_NCHANNELS_PER_PEER=32
+export VLLM_RPC_TIMEOUT=1800000
+export NCCL_IB_TIMEOUT=30
+
+export VLLM_MLA_DISABLE=0
+export VLLM_USE_FLASH_MLA=1
+
+echo "分配的节点列表: $SLURM_NODELIST"
+echo "主节点地址: \$MASTER_ADDR"
+echo "主节点端口: \$MASTER_PORT"
+echo "总进程数: \$WORLD_SIZE"
+sleep \$((RANK*3))
+DISTRIBUTED_ARGS="
+    --nproc_per_node=8 \
+    --nnodes=\$SLURM_JOB_NUM_NODES \
+    --node-rank=\${RANK} \
+    --master_addr=\${MASTER_ADDR} \
+    --master_port=\${MASTER_PORT}
+"
+torchrun \$DISTRIBUTED_ARGS /path/of/llama-factory/src/train.py   \
+    --deepspeed /path/of/deepspeed/ds_z3_config.json \
+    --stage grpo \
+    --do_train \
+    --finetuning_type freeze \
+    --freeze_trainable_layers 5 \
+    --freeze_trainable_modules all \
+    --model_name_or_path deepseek-ai/DeepSeek-R1-Distill-Llama-70B \
+    --dataset_dir /path/of/llama-factory-0923/data/ \
+    --dataset dapo_math,hiyouga-math12k \
+    --max_samples 20000 \
+    --template deepseekr1 \
+    --output_dir /path/of/saves/DeepSeek-R1-Distill-Llama-70B-0923/grpo/full/ \
+    --overwrite_output_dir \
+    --trust_remote_code \
+    --warmup_ratio 0.1 \
+    --max_grad_norm 1.0 \
+    --weight_decay 0.1 \
+    --repetition_penalty 50 \
+    --top_k 50 \
+    --top_p 0.8 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --preprocessing_num_workers 16 \
+    --ddp_timeout 120000000 \
+    --learning_rate 5e-3 \
+    --lr_scheduler_type cosine \
+    --optim paged_adamw_32bit \
+    --logging_steps 1 \
+    --cutoff_len 8192 \
+    --save_steps 100 \
+    --plot_loss True \
+    --num_train_epochs 1 \
+    --bf16 \
+    --seed 42 \
+    --report_to none \
+    --save_only_model
+EOF
+done
+wait
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sbatch_vllm.sh

+#!/bin/bash
+#SBATCH --job-name=grpo_vllm   # 作业名称
+#SBATCH --output=logs/grpo_vllm_%j.out # 输出日志文件
+#SBATCH --error=logs/grpo_vllm_%j.out  # 错误日志文件
+#SBATCH --nodes=1                       # 使用节点数量
+#SBATCH --qos=dcudvp
+#SBATCH --gres=dcu:8                      # 每节点 8 张 DCU
+#SBATCH --cpus-per-task=32                # 每个任务分配 32 个 CPU
+#SBATCH --partition=dcu                   # 使用 DCU 分区sinfo
+#SBATCH --ntasks-per-node=1
+#SBATCH --mem=480G
+#SBATCH --nodelist=xxxxxx  # 指定节点
+
+source ~/packages/dtk-25.04.1/env.sh
+source ~/miniconda3/etc/profile.d/conda.sh
+conda activate grpo
+
+export DISABLE_VERSION_CHECK=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+
+export NCCL_ALGO=Ring
+export NCCL_PROTO=Simple
+export NCCL_DEBUG=INFO
+export NCCL_P2P_LEVEL=SYS
+export NCCL_IB_DISABLE=1
+export VLLM_RPC_TIMEOUT=1800000
+export NCCL_IB_TIMEOUT=30
+export VLLM_MLA_DISABLE=0
+export VLLM_USE_FLASH_MLA=1
+export NCCL_SOCKET_IFNAME=ibxxxx # ifconfig查看实际IB网口名
+export NCCL_MIN_NCHANNELS=32
+export NCCL_MAX_NCHANNELS=32
+export NCCL_MIN_P2P_NCHANNELS=32
+export NCCL_MAX_P2P_NCHANNELS=32
+export NCCL_NCHANNELS_PER_PEER=32
+
+
+trl vllm-serve --model deepseek-ai/DeepSeek-R1-Distill-Llama-70B --tensor-parallel-size 8 --gpu_memory_utilization 0.8 --port 8001
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start_vllm_serve.sh

+#!/bin/bash
+export DISABLE_VERSION_CHECK=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export LLAMA_NN=0
+export HF_ENDPOINT=https://hf-mirror.com
+export VLLM_MLA_DISABLE=0
+export VLLM_USE_FLASH_MLA=1
+
+export NCCL_SOCKET_IFNAME=ibp58s0
+export NCCL_DEBUG=INFO
+export NCCL_ALGO=Ring
+export NCCL_PROTO=Simple
+export NCCL_MIN_NCHANNELS=32
+export NCCL_MAX_NCHANNELS=32
+export NCCL_MIN_P2P_NCHANNELS=32
+export NCCL_MAX_P2P_NCHANNELS=32
+export NCCL_NCHANNELS_PER_PEER=32
+export VLLM_RPC_TIMEOUT=1800000
+export NCCL_IB_TIMEOUT=30
+
+
+# export VLLM_WORKER_MULTIPROC_METHOD="spawn"
+
+
+trl vllm-serve --model llama3/Meta-Llama-3-70B-Instruct --tensor-parallel-size 8 --gpu_memory_utilization 0.8 --port 8001

train.sh

+#!/bin/bash
+export DISABLE_VERSION_CHECK=1
+export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 # 默认节点8张卡
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export HF_ENDPOINT=https://hf-mirror.com
+
+export MASTER_ADDR=XXXXXX ## 实际启动mastet节点 hostname或者IP地址
+export MASTER_PORT=29569
+export RANK=$1
+
+export NCCL_SOCKET_IFNAME=ibxxxxx # ifconfig查看实际IB网口名
+export NCCL_DEBUG=INFO
+export NCCL_ALGO=Ring
+export NCCL_PROTO=Simple
+export NCCL_MIN_NCHANNELS=32
+export NCCL_MAX_NCHANNELS=32
+export NCCL_MIN_P2P_NCHANNELS=32
+export NCCL_MAX_P2P_NCHANNELS=32
+export NCCL_NCHANNELS_PER_PEER=32
+export VLLM_RPC_TIMEOUT=1800000
+export NCCL_IB_TIMEOUT=30
+
+export VLLM_MLA_DISABLE=0
+export VLLM_USE_FLASH_MLA=1
+
+## nnodes 代表几台机器，请根据实际情况填写数量,eg: 2，代表2台机器
+torchrun --nproc_per_node=8 \
+    --nnodes=xxxx\
+    --node-rank=\${RANK} \
+    --master_addr=\${MASTER_ADDR} \
+    --master_port=\${MASTER_PORT} \
+    src/train.py   \
+    --deepspeed ~/GRPO/deepspeed/ds_z3_config.json \
+    --stage grpo \
+    --do_train \
+    --finetuning_type freeze \
+    --freeze_trainable_layers 5 \
+    --freeze_trainable_modules all \
+    --model_name_or_path deepseek-ai/DeepSeek-R1-Distill-Llama-70B \
+    --dataset dapo_math,hiyouga-math12k \
+    --max_samples 20000 \
+    --template deepseekr1 \
+    --output_dir saves/DeepSeek-R1-Distill-Llama-70B-0923/grpo/full/ \
+    --overwrite_output_dir \
+    --trust_remote_code \
+    --warmup_ratio 0.1 \
+    --max_grad_norm 1.0 \
+    --weight_decay 0.1 \
+    --repetition_penalty 50 \
+    --top_k 50 \
+    --top_p 0.8 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --preprocessing_num_workers 16 \
+    --ddp_timeout 120000000 \
+    --learning_rate 5e-3 \
+    --lr_scheduler_type cosine \
+    --logging_steps 1 \
+    --cutoff_len 8192 \
+    --save_steps 100 \
+    --plot_loss True \
+    --num_train_epochs 1 \
+    --bf16 \
+    --seed 42 \
+    --report_to none \
+    --save_only_model
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