Comparing with DDP, FSDP reduces GPU memory footprint by sharding model parameters, gradients, and optimizer states. It makes it feasible to train models that cannot fit on a single GPU. 这一突破性技术最初由微软的 ZeRO （Zero Redundancy Optimizer） 提出，并成为了 PyTorch FSDP（Fully Sharded Data Parallel）的理论基础。 光看上面的描述，初学者容易有的疑惑就是 . FSDP 是一种数据并行，它将模型参数、优化器状态和梯度分片到 DDP 秩中。 使用 FSDP 进行训练时，GPU 内存占用比在所有工作节点上使用 DDP 进行训练时更小。 这使得训练一些非常大的模型成为 .
完全分片数据并行（FSDP） 是一种数据并行方法， 它将模型的参数、梯度和优化器状态在可用 GPU（也称为 Worker 或 rank）的数量上进行分片。 与 分布式数据并行（DDP） 不同， FSDP 减 . 5 days ago · 通过深入理解零冗余优化器 (ZeRO) 和全分片数据并行 (FSDP)，掌握分布式大语言模型训练的核心。本教程涵盖内存管理、从零开始的实现逻辑以及 PyTorch 实战代码，助你攻克 AI 开发中的 . Sep 17, 2025 · 在大 模型 训练（Large Language Model Training）领域， FSDP、FSDP2 和 Megatron-LM 是三种主流的分布式训练框架或技术，它们用于解决超大规模模型在多GPU/多节点环境下训练时 .
Jul 14, 2025 · FSDP 可以被视为 DDP 的 all-reduce 分解为 reduce-scatter 和 all-gather 的操作。 与 FSDP1 相比，FSDP2 的 优点： 将分片参数表示为在 dim-i 上分片的 DTensor，从而可以轻松作单个 . Aug 25, 2025 · In some FSDP configurations, the forward pass can be preformed in parallel by loading the next MP (in this case MP 2), which further accelerates training. However, this also increases . Fully Sharded Data Parallel (FSDP) 是一种结合了数据并行和模型并行优点的分布式训练 并行 方法。 与 DistributedDataParallel (DDP) 不同，FSDP通过不在每个GPU上复制模型来节省更多内存。 它将模型 .
Apr 21, 2023 · In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training.
- Getting Started with Fully Sharded Data Parallel (FSDP2).
- In some FSDP configurations, the forward pass can be preformed in parallel by loading the next MP (in this case MP 2), which further accelerates training.
- In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training.
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