Opacus Advances Private Training of Large Models
Opacus has introduced critical improvements enabling more memory-efficient private training of large-scale models, particularly large language models (LLMs).
The key advancement is Fast Gradient Clipping (FGC) and Ghost Clipping (GC), which allow gradient clipping without instantiating per-sample gradients, reducing the memory overhead of differential privacy stochastic gradient descent (DP-SGD).
Despite these innovations, training very large models remains challenging, especially when scaling across multiple GPUs. Opacus currently uses Differentially Private Distributed Data Parallel (DPDDP), which replicates the model on each GPU, causing high memory use for large models. To address this, Opacus is incorporating Fully Sharded Data Parallel (FSDP), which shards model parameters, gradients, and optimizer states across GPUs, significantly improving memory efficiency and scalability for private training.
Parallelism Strategies for Large Language Models
Scaling private training depends on model size and parallelism strategy. For models under 10 billion parameters, 1D parallelism such as DPDDP or FSDP suffices. Medium-sized models (10-100 billion parameters) benefit from 2D parallelism combining FSDP with Tensor Parallelism (TP).
Very large models over 100 billion parameters use 4D parallelism combining FSDP, TP, Pipeline Parallelism (PP), and Context Parallelism (CP).
By adopting FSDP, Opacus prepares to support these advanced parallelism schemes, enabling efficient private training or fine-tuning of LLaMA and other large-scale LLMs. This is crucial as models grow beyond the capacity of naive replicated approaches like DPDDP.
FSDP Enables Memory Efficiency with Gradient Clipping
Fully Sharded Data Parallelism (FSDP) shards model parameters, gradients, and optimizer states across GPUs, reducing per-GPU memory usage. During training, FSDP gathers full parameters of one layer at a time to perform forward and backward passes, then discards them to keep memory use low. This design reduces peak memory to roughly the size of one layer’s parameters plus activations, rather than the entire model. FSDP incurs communication overhead but overlaps it with computation to minimize latency impact. Integrating FGC and GC with FSDP allows Opacus to perform per-sample gradient norm computations efficiently without storing full per-sample gradients, which greatly reduces memory demands during DP-SGD training.
Using FSDP with Ghost Clipping in Opacus
Opacus users can enable FSDP with Ghost Clipping by wrapping their model with FSDP2Wrapper and specifying grad_sample_mode=”ghost_fsdp” when calling PrivacyEngine’s make_private method. FSDP2Wrapper applies the second version of FSDP, which supports the two backward passes required by Ghost Clipping. The training loop remains the same as standard PyTorch, making adoption straightforward. This setup shards model states across GPUs, applies noise to parameter shards, and performs optimizer steps locally, enabling scalable private training with differential privacy guarantees. The provided example code demonstrates launching distributed training on multiple GPUs with this configuration.
FSDP2 Achieves Over 2.5x Larger Batch Size on GPT2 Models
Memory tests on GPT2 models show that FSDP2 supports significantly larger batch sizes compared to DPDDP when using Ghost Clipping. For a 1.5 billion parameter GPT2 model trained on 1×8 A100 40GB GPUs, FSDP2 achieves a maximum batch size 2.6 times larger than DPDDP. This improvement arises because FSDP2 shards model and optimizer states, reducing memory footprint on each device. Detailed memory breakdowns reveal that after model initialization, FSDP2 uses 8 times less total memory than DPDDP. During backward passes and optimizer steps, FSDP2 peak memory scales with model size divided by number of GPUs, whereas DPDDP scales directly with model size, making FSDP2 more advantageous for large models.
Latency Trade
Latency Trade-offs for LoRA Fine-Tuning of LLaMA-3 8B. Latency benchmarks for LoRA fine-tuning of the LLaMA-3 8B model (6.8 million trainable parameters) highlight trade-offs between DPDDP and FSDP2.
Using 1×8 A100 80GB GPUs on the Tiny Shakespeare dataset, FSDP2 supports almost twice the batch size compared to DPDDP with Ghost Clipping but has about 60% throughput for the same effective batch size. This suggests FSDP2 incurs communication overhead affecting throughput, despite memory advantages. When no gradient accumulation is used, DPDDP achieves 22.66 samples/second with Ghost Clipping, while FSDP2 achieves 17.36 samples/second at half the batch size and 21.19 samples/second at double the batch size. For setups with large sequence lengths (4096 tokens), which DPDDP cannot handle, FSDP2 becomes necessary despite latency trade-offs.
Practical Recommendations for Private Training with Opacus
For practitioners aiming to train large-scale models privately with Opacus, FSDP2 with Ghost Clipping offers a clear path to scaling beyond DPDDP’s memory limits. Models with over 1 billion parameters benefit most, as FSDP2 enables 2.6x larger batch sizes on high-memory GPUs like A100 40GB. The integration retains a familiar PyTorch training loop, easing adoption. However, throughput may be lower than DPDDP in some LoRA fine-tuning scenarios, so trade-offs between maximum batch size and latency must be considered. For extremely large models or long sequences exceeding DPDDP capacity, FSDP2 is essential. As Opacus evolves, support for 2D and 4D parallelism will further enable private training of models with tens or hundreds of billions of parameters.
Step Guide
Summary of Key Quantitative Results. – FSDP2 achieves a 2.6x larger batch size than DPDDP on a 1.5B parameter GPT2 model using Ghost Clipping on 1×8 A100 40GB GPUs. – Memory after model init with FSDP2 is 0.78 GB vs 5.93 GB for DPDDP, an 8x reduction. – Peak memory during optimizer step is 3.98 GB for FSDP2 vs 34.15 GB for DPDDP. – LoRA fine-tuning of LLaMA-3 8B on 1×8 A100 80GB GPUs shows DPDDP throughput of 22.66 samples/s and FSDP2 throughput of 17.36 samples/s at comparable batch sizes. – FSDP2 enables training with sequence lengths DPDDP cannot support, critical for very long context models. These data-driven insights demonstrate that Opacus’s integration of FSDP with advanced gradient clipping techniques offers a practical and scalable solution for private training of large language models under current hardware constraints.
