# pytorch-fsdp2

Teaches a coding agent to wire PyTorch FSDP2 into a training loop: init, sharding, mixed precision and distributed checkpoints.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 958 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/08-distributed-training/pytorch-fsdp2/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/pytorch-fsdp2

## What it does

Reach for this skill when a model no longer fits on one GPU — weights, gradients and optimizer state combined. The agent gets instructions for hooking up `torch.distributed.fsdp.fully_shard` and the `FSDPModule` methods it adds to modules in place.

The procedure is spelled out step by step: launch with `torchrun` and pin the CUDA device from `LOCAL_RANK`, initialize the process group, then shard bottom-up — transformer blocks first, root module last. The optimizer is built after sharding so it holds DTensor parameters. Very large models can start on `meta` and materialize later with `to_empty`. For checkpoints the skill points to Distributed Checkpoint or the distributed state dict helpers.

One caveat worth knowing before you let the agent touch your script: DCP itself warns against expecting strict checkpoint compatibility across PyTorch versions. And if your PyTorch build has no FSDP2 stack, none of this applies — check that first.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pytorch-fsdp2 -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/pytorch-fsdp2/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/pytorch-fsdp2/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pytorch-fsdp2 -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/pytorch-fsdp2/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pytorch-fsdp2 -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/pytorch-fsdp2/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs pytorch-fsdp2
```

2. **Manually: put the files in place**
- File: `.github/skills/pytorch-fsdp2/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pytorch-fsdp2 -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 08-distributed-training/pytorch-fsdp2
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/pytorch-fsdp2/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pytorch-fsdp2 -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/08-distributed-training/pytorch-fsdp2/SKILL.md

## When to use it
- train a model that does not fit on a single GPU
- migrate a script from FSDP1 to per-parameter DTensor sharding
- set up mixed precision and CPU offload for the training loop
- save and load checkpoints from several ranks in parallel
- build a DeviceMesh for data and tensor parallelism

## Example prompts
- add FSDP2 to my train.py, the model won't fit on one GPU
- rewrite the FSDP1 wrapper to use fully_shard bottom-up
- set up DCP checkpointing for this training loop

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 7f4d6d97435be6fb.

BEGIN THIRD-PARTY FILE 7f4d6d97435be6fb
```text
---
name: pytorch-fsdp2
description: Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [PyTorch, FSDP2, Fully Sharded Data Parallel, Distributed Training, DTensor, Device Mesh, Sharded Checkpointing, Mixed Precision, Offload, Torch Distributed]
dependencies: [torch]
---

# Skill: Use PyTorch FSDP2 (`fully_shard`) correctly in a training script

This skill teaches a coding agent how to **add PyTorch FSDP2** to a training loop with correct initialization, sharding, mixed precision/offload configuration, and checkpointing.

> FSDP2 in PyTorch is exposed primarily via `torch.distributed.fsdp.fully_shard` and the `FSDPModule` methods it adds in-place to modules. See: `references/pytorch_fully_shard_api.md`, `references/pytorch_fsdp2_tutorial.md`.

---

## When to use this skill

Use FSDP2 when:
- Your model **doesn’t fit** on one GPU (parameters + gradients + optimizer state).
- You want an eager-mode sharding approach that is **DTensor-based per-parameter sharding** (more inspectable, simpler sharded state dicts) than FSDP1.  
- You may later compose DP with **Tensor Parallel** using **DeviceMesh**.

Avoid (or be careful) if:
- You need strict backwards-compatible checkpoints across PyTorch versions (DCP warns against this).
- You’re forced onto older PyTorch versions without the FSDP2 stack.

## Alternatives (when FSDP2 is not the best fit)

- **DistributedDataParallel (DDP)**: Use the standard data-parallel wrapper when you want classic distributed data parallel training.
- **FullyShardedDataParallel (FSDP1)**: Use the original FSDP wrapper for parameter sharding across data-parallel workers.

Reference: `references/pytorch_ddp_notes.md`, `references/pytorch_fsdp1_api.md`.

---

## Contract the agent must follow

1. **Launch with `torchrun`** and set the CUDA device per process (usually via `LOCAL_RANK`).  
2. **Apply `fully_shard()` bottom-up**, i.e., shard submodules (e.g., Transformer blocks) before the root module.  
3. **Call `model(input)`**, not `model.forward(input)`, so the FSDP2 hooks run (unless you explicitly `unshard()` or register the forward method).  
4. **Create the optimizer after sharding** and make sure it is built on the **DTensor parameters** (post-`fully_shard`).  
```
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