| # Elastic |
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| ## 安装依赖 |
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| 集群部署K8S,并在集群中部署DLrover,[DLRover](https://github.com/intelligent-machine-learning/dlrover), |
| `pip install dlrover && pip install tornado && pip install kubernetes && pip install ms-swift` |
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| 经过反复测试验证的训练镜像中的其它依赖以及版本： |
| deepspeed 0.16.5（需参考https://github.com/deepspeedai/DeepSpeed/pull/7585/files 修复universal checkpoint 相关问题） |
| pytorch 2.6.0 |
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| ## 如何启动 |
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| 通过在`--callbacks`中添加`deepspeed_elastic`（可选`graceful_exit`）启用弹性训练，并配置DeepSpeed弹性参数。 |
| 命令组成=dlrover-run +dlrover 命令参数+swift 启动命令 +swift参数,dlrover-run除自定义的参数外，其他参数与torchrun一致； |
| dlrover-run 参数如下: |
| ``` |
| usage: dlrover-run [-h] [--nnodes NNODES] [--nproc-per-node NPROC_PER_NODE] |
| [--rdzv-backend RDZV_BACKEND] [--rdzv-endpoint RDZV_ENDPOINT] [--rdzv-id RDZV_ID] |
| [--rdzv-conf RDZV_CONF] [--standalone] [--max-restarts MAX_RESTARTS] |
| [--monitor-interval MONITOR_INTERVAL] [--start-method {spawn,fork,forkserver}] |
| [--role ROLE] [-m] [--no-python] [--run-path] [--log-dir LOG_DIR] [-r REDIRECTS] |
| [-t TEE] [--local-ranks-filter LOCAL_RANKS_FILTER] [--node-rank NODE_RANK] |
| [--master-addr MASTER_ADDR] [--master-port MASTER_PORT] [--local-addr LOCAL_ADDR] |
| [--logs-specs LOGS_SPECS] [--precheck {0,1,2}] [--node_unit NODE_UNIT] |
| [--auto_config] [--auto_tunning] [--exclude-straggler] [--save_at_breakpoint] |
| [--accelerator {nvidia.com/gpu,ascend-npu}] [--training_port TRAINING_PORT] |
| [--switchbox-check] [--box-pairs PAIR [PAIR ...]] [--min-bandwidth MIN_BANDWIDTH] |
| [--min-channels MIN_CHANNELS] [--numa-affinity] [--network-check] |
| [--comm-perf-test] [--ucp_device_type UCP_DEVICE_TYPE] |
| training_script |
| |
| ``` |
| 在弹性训练中我们需要关注的参数为： |
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| --nnodes NNODES Number of nodes, or the range of nodes in form |
| <minimum_nodes>:<maximum_nodes>. |
| |
| --nproc-per-node NPROC_PER_NODE Number of processes per node. |
| 示例: |
|
|
| ```bash |
| model=your model path |
| dataset=your dataset |
| output= your output dir |
| export CUDA_VISIBLE_DEVICES=0 根据实际使用的GPU情况设置 |
| deepspeed_config_or_type=deepspeed类型或者配置文件的路径，如 zero1 或者/xxx/ms-swift/swift/llm/ds_config/zero1.json |
| |
| dlrover-run --nnodes 1:$NODE_NUM --nproc_per_node=1 \ |
| /opt/conda/lib/python3.10/site-packages/swift/cli/sft.py --model $model \ |
| --model_type qwen3 \ |
| --tuner_type lora \ |
| --torch_dtype bfloat16 \ |
| --dataset $dataset \ |
| --num_train_epochs 4 \ |
| --per_device_train_batch_size 1 \ |
| --per_device_eval_batch_size 1 \ |
| --learning_rate 5e-7 \ |
| --gradient_accumulation_steps 8 \ |
| --eval_steps 500 \ |
| --save_steps 10 \ |
| --save_total_limit 20 \ |
| --logging_steps 1 \ |
| --output_dir $output \ |
| --warmup_ratio 0.01 \ |
| --dataloader_num_workers 4 \ |
| --temperature 1.0 \ |
| --system 'You are a helpful assistant.' \ |
| --lora_rank 8 \ |
| --lora_alpha 32 \ |
| --target_modules all-linear \ |
| --dataset_num_proc 1 \ |
| --use_flash_ckpt true \ |
| --callbacks deepspeed_elastic graceful_exit \ |
| --deepspeed $deepspeed_config_or_type \ |
| ``` |
|
|
| ## 配置文件示例 |
| 默认情况下的zero1为以下示例配置, |
|
|
| ```json |
| { |
| "fp16": { |
| "enabled": "auto", |
| "loss_scale": 0, |
| "loss_scale_window": 1000, |
| "initial_scale_power": 16, |
| "hysteresis": 2, |
| "min_loss_scale": 1 |
| }, |
| |
| "bf16": { |
| "enabled": "auto" |
| }, |
| |
| "zero_optimization": { |
| "stage": 1, |
| "offload_optimizer": { |
| "device": "none", |
| "pin_memory": true |
| }, |
| "allgather_partitions": true, |
| "allgather_bucket_size": 2e8, |
| "overlap_comm": false, |
| "reduce_scatter": true, |
| "reduce_bucket_size": 2e8, |
| "contiguous_gradients": true |
| }, |
| |
| "gradient_accumulation_steps": "auto", |
| "gradient_clipping": "auto", |
| "steps_per_print": 2000, |
| "train_batch_size": "auto", |
| "train_micro_batch_size_per_gpu": "auto", |
| "wall_clock_breakdown": false, |
| "elasticity": { |
| "ignore_non_elastic_batch_info": true, |
| "enabled": true, |
| "max_train_batch_size": 8, |
| "micro_batch_sizes": [ |
| 4, |
| 2 |
| ], |
| "min_gpus": 1, |
| "max_gpus": 4, |
| "min_time": 20, |
| "version": 0.1 |
| } |
| } |
| ``` |
|
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| 如果用户需要自定义，可以在启动命令中deepspeed_config_or_type指定自定义的zero1.json的存放路径,其中弹性相关的配置为： |
| ```json |
| ... |
| |
| "elasticity": { |
| "ignore_non_elastic_batch_info": true, |
| "enabled": true, |
| "max_train_batch_size": 8, |
| "micro_batch_sizes": [ |
| 4, |
| 2 |
| ], |
| "min_gpus": 1, |
| "max_gpus": 4, |
| "min_time": 20, |
| "version": 0.1 |
| } |
| ``` |
| |
| - ignore_non_elastic_batch_info：代表在elasticity里的配置会忽略外层的batch_size相关的配置，训练过程中会根据实际的训练进程个数实时修改batch_size等相关的参数 |
| 计算原则为： |
| global-training-batch-size = micro-batch-size * gradient-accumulation-steps * world-size |
| - max_train_batch_size：最大batch_size数 |
| - micro_batch_sizes：elasticity下允许的每卡micro-batch size列表，相当于train_micro_batch_size_per_gpu的候选值 |
| - min_gpus：最小gpu数目 |
| - max_gpus：最大gpu数目 |
| 更详细的内容见：[Deepspeed](https://www.deepspeed.ai/docs/config-json/#elastic-training-config-v01-and-v02) |
| |
| |
| ## 启动训练 |
| |
| ```yaml |
| --- |
| apiVersion: elastic.iml.github.io/v1alpha1 |
| kind: ElasticJob |
| metadata: |
| name: deepspeed-elastic-swift |
| namespace: dlrover |
| spec: |
| distributionStrategy: AllreduceStrategy |
| optimizeMode: single-job |
| replicaSpecs: |
| worker: |
| replicas: 1 #【这里需要与启动命令中的--nnodes NNODES的最大值一致】 |
| template: |
| spec: |
| restartPolicy: Never |
| containers: |
| - name: main |
| image: #【训练镜像，需要安装deepspeed,dlrover 和swift 】 |
| imagePullPolicy: IfNotPresent |
| command: |
| - /bin/bash |
| - -c |
| - sh start.sh # 启动脚本 |
| resources: |
| limits: |
| cpu: '8' |
| memory: 16Gi |
| nvidia.com/gpu: '1' |
| volumeMounts: |
| - mountPath: /model |
| name: volume-model |
| - mountPath: /dev/shm |
| name: volume-shm |
| restartPolicy: Never |
| volumes: |
| - hostPath: |
| path: /model |
| type: Directory |
| name: volume-model |
| - emptyDir: |
| medium: Memory |
| sizeLimit: 200Gi |
| name: volume-shm |
| |
| ``` |
| |
