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本文详细介绍 DeepSpeed 配置文件#xff0c;结合 4 卡 3090 的实际使用场景#xff0c;重点解释各个参数的含义#xff0c;并提供应对爆显存的方案。 DeepSpeed 配置文件详解#xff1a;从基础到实战
DeepSpeed 是用于加速大规模分布式训练的重要工具#xff0c…中文版
本文详细介绍 DeepSpeed 配置文件结合 4 卡 3090 的实际使用场景重点解释各个参数的含义并提供应对爆显存的方案。 DeepSpeed 配置文件详解从基础到实战
DeepSpeed 是用于加速大规模分布式训练的重要工具其灵活的配置文件是实现高效训练的关键。在本篇博客中我们将深入解读 DeepSpeed 配置文件的结构和关键参数结合 4 卡 3090 的实际训练场景探讨如何优化配置解决爆显存问题。 1. 配置文件的结构
DeepSpeed 的配置文件一般以 JSON 格式定义包括以下几个核心部分
bf16/fp16 配置决定是否启用混合精度训练。ZeRO 优化配置用于控制内存优化策略。训练相关参数例如批量大小、梯度累积步数等。
以下是一个典型的配置文件示例
{bf16: {enabled: true},zero_optimization: {stage: 2,overlap_comm: true,contiguous_gradients: false,reduce_bucket_size: 5e5,sub_group_size: 5e5},gradient_accumulation_steps: 4,train_micro_batch_size_per_gpu: 1,gradient_clipping: 1.0
}2. 关键参数解析
bf16.enabled
含义启用 BF16 混合精度训练。影响显著减少显存占用提升训练速度。
zero_optimization.stage
含义指定 ZeRO 优化的阶段。 Stage 1优化梯度存储。Stage 2进一步优化优化器状态存储。Stage 3支持模型分片。 推荐对于 4 卡 3090优先选择 Stage 2在显存允许的情况下使用 Stage 3。
overlap_comm
含义启用通信与计算的重叠减少通信开销。建议在多卡场景中始终开启。
contiguous_gradients
含义是否在内存中存储连续梯度。优点开启后可减少内存碎片化提高通信效率。缺点增加显存开销。建议若显存不足可将其设置为 false。
reduce_bucket_size
含义定义一次通信中参数分片的最大大小。单位字节。默认值5e6即 5 MB。调整 若显存不足减小值至 1e5 或 5e5。如果通信瓶颈明显可适当增大值。
sub_group_size
含义设置通信子组的参数分片大小。默认值1e8即 100 MB。调整 小模型5e5 或更低。大模型可根据显存容量调试通常 1e6 至 1e7。
gradient_accumulation_steps
含义设置梯度累积步数减少单步的显存压力。建议逐步增加值如从 4 到 8但需注意总批量大小的变化。
train_micro_batch_size_per_gpu
含义每张 GPU 的微批量大小。建议在显存不足时减小如从 4 降为 1。
gradient_clipping
含义限制梯度范数防止梯度爆炸。推荐值1.0。 3. 针对 4 卡 3090 的优化建议 显存不足问题解决方法 减小 reduce_bucket_size 和 sub_group_sizereduce_bucket_size: 1e5,
sub_group_size: 5e5降低 train_micro_batch_size_per_gputrain_micro_batch_size_per_gpu: 1增大 gradient_accumulation_stepsgradient_accumulation_steps: 8禁用 contiguous_gradientscontiguous_gradients: false检查 NCCL 环境变量 确保以下变量已正确设置避免通信问题导致显存不足。 export NCCL_BLOCKING_WAIT1
export NCCL_ASYNC_ERROR_HANDLING1
export NCCL_TIMEOUT10800启用 CPU Offloading如果必要 对于显存严重不足的场景可将部分优化器状态卸载至 CPU。 offload_optimizer: {device: cpu,pin_memory: true
}4. 实验结果分析与日志监控
在训练过程中通过以下设置获取详细的资源占用信息
wall_clock_breakdown: true并结合 DeepSpeed 的日志分析显存使用、通信效率等关键指标。 通过合理配置 DeepSpeed 配置文件结合具体的硬件资源和任务需求可以显著提升训练效率减少显存压力。
英文版
This article is about explaining DeepSpeed configuration files, focusing on practical usage with a 4x 3090 GPU setup. This includes a breakdown of key parameters like contiguous_gradients, reduce_bucket_size, and sub_group_size, as well as solutions for handling out-of-memory (OOM) errors. DeepSpeed Configuration Files: A Comprehensive Guide
DeepSpeed offers advanced optimization features like ZeRO (Zero Redundancy Optimizer) to enable efficient large-scale model training. This post will delve into configuring DeepSpeed for optimal performance, with examples and tips tailored to a 4x NVIDIA 3090 GPU setup. 1. Key Parameters in a DeepSpeed Configuration File
Below is an example configuration file for ZeRO Stage 2 optimization, designed for fine-tuning large models:
{zero_optimization: {stage: 2,overlap_comm: true,contiguous_gradients: false,reduce_bucket_size: 5e5,sub_group_size: 5e5},gradient_accumulation_steps: 4,train_micro_batch_size_per_gpu: 1,gradient_clipping: 1.0
}Let’s break down the parameters:
(1) zero_optimization.stage
Defines the ZeRO optimization stage: Stage 2: Optimizes optimizer states and gradients across GPUs, reducing memory usage.Use Stage 3 for more aggressive memory savings by offloading parameters to CPU, if applicable.
(2) overlap_comm
Default: trueEnables overlapping communication with computation, improving efficiency during distributed training.
(3) contiguous_gradients
Default: falseWhen true, all gradients are stored contiguously in memory. Benefit: Faster gradient reductions.Drawback: Increases memory usage.Recommendation: Set to false if facing OOM issues.
(4) reduce_bucket_size
Defines the size of gradient buckets for all-reduce operations. Smaller values (e.g., 5e5) reduce memory pressure but may slightly slow down training.Larger values improve speed but require more memory.
(5) sub_group_size
Controls sub-grouping of gradients during communication. Default: A large value (e.g., 1e9), meaning no sub-grouping.Recommendation: Reduce to 5e5 or lower for better memory efficiency.
(6) gradient_accumulation_steps
Number of steps to accumulate gradients before performing a backward pass. Higher values effectively increase the batch size without increasing per-GPU memory load.
(7) train_micro_batch_size_per_gpu
Batch size per GPU per step. Recommendation: Start with a small value (e.g., 1) and scale up gradually. 2. Handling Out-of-Memory (OOM) Errors
Training large models like Google Gemma-2-2B on GPUs with limited memory (24 GB, such as NVIDIA 3090) often results in OOM errors. Here are optimization strategies:
(1) Reduce train_micro_batch_size_per_gpu
Start with 1 and only increase if memory allows.
(2) Lower reduce_bucket_size and sub_group_size
Decrease both to 1e5 or 5e4. This reduces the memory footprint during gradient reduction at the cost of slightly increased communication overhead.
(3) Enable offload_optimizer or offload_param (for ZeRO Stage 3)
Offload optimizer states or parameters to CPU if memory remains insufficient.Example configuration for optimizer offloading:{zero_optimization: {stage: 3,offload_optimizer: {device: cpu,pin_memory: true}}
}(4) Use Gradient Checkpointing
Activates checkpointing for intermediate activations to save memory during backpropagation.from deepspeed.runtime.activation_checkpointing import checkpointing_config
checkpointing_config(partition_activationsTrue,contiguous_memory_optimizationFalse
)(5) Mixed Precision Training (bf16 or fp16)
Use bf16 for better memory efficiency with minimal precision loss.
(6) Increase gradient_accumulation_steps
Accumulate gradients over more steps to reduce the batch size processed per GPU.
(7) Reduce max_seq_length
Shorten sequence length (e.g., 512 or 768 tokens) to decrease memory usage. 3. Practical Example: Fine-Tuning on 4x NVIDIA 3090 GPUs
The following accelerate command illustrates how to combine the above settings for fine-tuning a large model:
accelerate launch \--mixed_precision bf16 \--num_machines 1 \--num_processes 4 \--machine_rank 0 \--main_process_ip 127.0.0.1 \--main_process_port 29400 \--use_deepspeed \--deepspeed_config_file configs/ds_config.json \--model_name_or_path google/gemma-2-2b \--tokenizer_name google/gemma-2-2b \--max_seq_length 768 \--per_device_train_batch_size 1 \--gradient_accumulation_steps 4 \--learning_rate 5e-6 \--num_train_epochs 1 \--output_dir output/sft_gemma24. Debugging Tips
Enable Detailed Logs: Set wall_clock_breakdown: true in the config file to identify bottlenecks.NCCL Tuning: Add environment variables to handle communication errors:export NCCL_BLOCKING_WAIT1
export NCCL_ASYNC_ERROR_HANDLING1Conclusion
DeepSpeed’s configuration is highly flexible, but tuning requires balancing memory efficiency and computational speed. By adjusting parameters like reduce_bucket_size, gradient_accumulation_steps, and leveraging ZeRO’s offloading capabilities, you can effectively train large models even on memory-constrained GPUs like the NVIDIA 3090.
后记
2024年11月27日22点08分于上海基于GPT4o大模型。
