TorchTitan - PyTorch Native Distributed LLM Pretraining

Quick start

TorchTitan is PyTorch's official platform for large-scale LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), achieving 65%+ speedups over baselines on H100 GPUs.

Installation:

# From PyPI (stable)
pip install torchtitan

# From source (latest features, requires PyTorch nightly)
git clone https://github.com/pytorch/torchtitan
cd torchtitan
pip install -r requirements.txt

Download tokenizer:

# Get HF token from https://huggingface.co/settings/tokens
python scripts/download_hf_assets.py --repo_id meta-llama/Llama-3.1-8B --assets tokenizer --hf_token=...

Start training on 8 GPUs:

CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh

Common workflows

Workflow 1: Pretrain Llama 3.1 8B on single node

Copy this checklist:

Single Node Pretraining:
- [ ] Step 1: Download tokenizer
- [ ] Step 2: Configure training
- [ ] Step 3: Launch training
- [ ] Step 4: Monitor and checkpoint

Step 1: Download tokenizer

python scripts/download_hf_assets.py \
  --repo_id meta-llama/Llama-3.1-8B \
  --assets tokenizer \
  --hf_token=YOUR_HF_TOKEN

Step 2: Configure training

Edit or create a TOML config file:

# llama3_8b_custom.toml
[job]
dump_folder = "./outputs"
description = "Llama 3.1 8B training"

[model]
name = "llama3"
flavor = "8B"
hf_assets_path = "./assets/hf/Llama-3.1-8B"

[optimizer]
name = "AdamW"
lr = 3e-4

[lr_scheduler]
warmup_steps = 200

[training]
local_batch_size = 2
seq_len = 8192
max_norm = 1.0
steps = 1000
dataset = "c4"

[parallelism]
data_parallel_shard_degree = -1  # Use all GPUs for FSDP

[activation_checkpoint]
mode = "selective"
selective_ac_option = "op"

[checkpoint]
enable = true
folder = "checkpoint"
interval = 500

Step 3: Launch training

# 8 GPUs on single node
CONFIG_FILE="./llama3_8b_custom.toml" ./run_train.sh