distributed-llm-pretraining-torchtitan

name	distributed-llm-pretraining-torchtitan
description	Pretrain LLMs at scale with PyTorch 4D parallelism.
version	1.0.1
author	Orchestra Research
license	MIT
dependencies	["torch>=2.6.0","torchtitan>=0.2.0","torchao>=0.5.0"]
platforms	["linux","macos"]
metadata	{"hermes":{"tags":["Model Architecture","Distributed Training","TorchTitan","FSDP2","Tensor Parallel","Pipeline Parallel","Context Parallel","Float8","Llama","Pretraining"]}}

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:

# Configs are selected by name from the Python config registry
# (torchtitan/models/llama3/config_registry.py), not by TOML path
MODULE=llama3 CONFIG=llama3_8b ./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

In torchtitan's current layout, run configs are defined in a Python config registry (torchtitan/models/llama3/config_registry.py) and selected by name via CONFIG=<name> (or --config <name>). To customize, register your own config in the registry, or override individual fields on the command line (e.g. --optimizer.lr 3e-4 --training.steps 1000).

The equivalent settings for an 8B run look like this (shown as fields; set them in the registry entry or as --section.key value overrides):

# fields for a llama3 8B run (register in config_registry.py or pass as --overrides)
[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 selected by name from the registry)
MODULE=llama3 CONFIG=llama3_8b ./run_train.sh

# Override individual fields on the command line
MODULE=llama3 CONFIG=llama3_8b ./run_train.sh --optimizer.lr 3e-4 --training.steps 1000

# Or explicitly with torchrun (run_train.sh wraps this)
torchrun --nproc_per_node=8 \
  -m torchtitan.train \
  --module llama3 --config llama3_8b

Step 4: Monitor and checkpoint

TensorBoard logs are saved to ./outputs/tb/:

tensorboard --logdir ./outputs/tb

Workflow 2: Multi-node training with SLURM

Multi-Node Training:
- [ ] Step 1: Configure parallelism for scale
- [ ] Step 2: Set up SLURM script
- [ ] Step 3: Submit job
- [ ] Step 4: Resume from checkpoint

Step 1: Configure parallelism for scale

For 70B model on 256 GPUs (32 nodes):

[parallelism]
data_parallel_shard_degree = 32  # FSDP across 32 ranks
tensor_parallel_degree = 8        # TP within node
pipeline_parallel_degree = 1      # No PP for 70B
context_parallel_degree = 1       # Increase for long sequences

Step 2: Set up SLURM script

#!/bin/bash
#SBATCH --job-name=llama70b
#SBATCH --nodes=32
#SBATCH --ntasks-per-node=8
#SBATCH --gpus-per-node=8

srun torchrun \
  --nnodes=32 \
  --nproc_per_node=8 \
  --rdzv_backend=c10d \
  --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \
  -m torchtitan.train \
  --module llama3 --config llama3_70b

Step 3: Submit job

sbatch multinode_trainer.slurm

Step 4: Resume from checkpoint

Training auto-resumes if checkpoint exists in configured folder.

Workflow 3: Enable Float8 training for H100s

Float8 provides 30-50% speedup on H100 GPUs.

Float8 Training:
- [ ] Step 1: Install torchao
- [ ] Step 2: Configure Float8
- [ ] Step 3: Launch with compile

Step 1: Install torchao

USE_CPP=0 pip install git+https://github.com/pytorch/ao.git

Step 2: Configure Float8

In the current torchtitan, Float8 is applied at config time via the quantization parameter in your model_registry() call inside the config registry (not via a [quantize.linear.float8] TOML section). Add a Float8LinearConverter.Config:

# in torchtitan/models/llama3/config_registry.py (your model_registry(...) call)
from torchtitan.components.quantization import Float8LinearConverter

model_spec = model_registry(
    "8B",
    quantization=[
        Float8LinearConverter.Config(
            recipe_name="rowwise",          # or "rowwise_with_gw_hp"
            filter_fqns=["output"],          # skip layers too small to benefit
            model_compile_enabled=True,      # requires torch.compile for competitive perf
        ),
    ],
)

Enable torch.compile in your run config too:

[compile]
enable = true
components = ["model", "loss"]

Step 3: Launch with compile

# Float8 config is baked into the registered config; just select it and enable compile
MODULE=llama3 CONFIG=llama3_8b ./run_train.sh --compile.enable

Workflow 4: 4D parallelism for 405B models

4D Parallelism (FSDP + TP + PP + CP):
- [ ] Step 1: Create seed checkpoint
- [ ] Step 2: Configure 4D parallelism
- [ ] Step 3: Launch on 512 GPUs

Step 1: Create seed checkpoint

Required for consistent initialization across PP stages:

NGPU=1 MODULE=llama3 CONFIG=llama3_405b ./run_train.sh \
  --checkpoint.enable \
  --checkpoint.create_seed_checkpoint \
  --parallelism.data_parallel_shard_degree 1 \
  --parallelism.tensor_parallel_degree 1 \
  --parallelism.pipeline_parallel_degree 1

Step 2: Configure 4D parallelism

[parallelism]
data_parallel_shard_degree = 8   # FSDP
tensor_parallel_degree = 8       # TP within node
pipeline_parallel_degree = 8     # PP across nodes
context_parallel_degree = 1      # CP for long sequences

[training]
local_batch_size = 32
seq_len = 8192

Step 3: Launch on 512 GPUs

# 64 nodes x 8 GPUs = 512 GPUs
srun torchrun --nnodes=64 --nproc_per_node=8 \
  -m torchtitan.train \
  --module llama3 --config llama3_405b

When to use vs alternatives

Use TorchTitan when:

• Pretraining LLMs from scratch (8B to 405B+)
• Need PyTorch-native solution without third-party dependencies
• Require composable 4D parallelism (FSDP2, TP, PP, CP)
• Training on H100s with Float8 support
• Want interoperable checkpoints with torchtune/HuggingFace

Use alternatives instead:

• Megatron-LM: Maximum performance for NVIDIA-only deployments
• DeepSpeed: Broader ZeRO optimization ecosystem, inference support
• Axolotl/TRL: Fine-tuning rather than pretraining
• LitGPT: Educational, smaller-scale training

Common issues

Issue: Out of memory on large models

Enable activation checkpointing and reduce batch size:

[activation_checkpoint]
mode = "full"  # Instead of "selective"

[training]
local_batch_size = 1

Or use gradient accumulation:

[training]
local_batch_size = 1
global_batch_size = 32  # Accumulates gradients

Issue: TP causes high memory with async collectives

Set environment variable:

export TORCH_NCCL_AVOID_RECORD_STREAMS=1

Issue: Float8 training not faster

Float8 only benefits large GEMMs. Filter small layers via the converter's filter_fqns:

from torchtitan.components.quantization import Float8LinearConverter

Float8LinearConverter.Config(
    # add "auto_filter_small_kn" to auto-skip layers too small to benefit
    filter_fqns=["attention.wk", "attention.wv", "output", "auto_filter_small_kn"],
    model_compile_enabled=True,
)

Issue: Checkpoint loading fails after parallelism change

Use DCP's resharding capability:

# Convert sharded checkpoint to single file
python -m torch.distributed.checkpoint.format_utils \
  dcp_to_torch checkpoint/step-1000 checkpoint.pt

Issue: Pipeline parallelism initialization

Create seed checkpoint first (see Workflow 4, Step 1).

Supported models

Model	Sizes	Status
Llama 3.1	8B, 70B, 405B	Production
Llama 4	Various	Experimental
DeepSeek V3	16B, 236B, 671B (MoE)	Experimental
GPT-OSS	20B, 120B (MoE)	Experimental
Qwen 3	Various	Experimental
Flux	Diffusion	Experimental

Performance benchmarks (H100)

Model	GPUs	Parallelism	TPS/GPU	Techniques
Llama 8B	8	FSDP	5,762	Baseline
Llama 8B	8	FSDP+compile+FP8	8,532	+48%
Llama 70B	256	FSDP+TP+AsyncTP	876	2D parallel
Llama 405B	512	FSDP+TP+PP	128	3D parallel

Advanced topics

FSDP2 configuration: See references/fsdp.md for detailed FSDP2 vs FSDP1 comparison and ZeRO equivalents.

Float8 training: See references/float8.md for tensorwise vs rowwise scaling recipes.

Checkpointing: See references/checkpoint.md for HuggingFace conversion and async checkpointing.

Adding custom models: See references/custom-models.md for TrainSpec protocol.

Resources

• GitHub: https://github.com/pytorch/torchtitan
• Paper: https://arxiv.org/abs/2410.06511
• ICLR 2025: https://iclr.cc/virtual/2025/poster/29620
• PyTorch Forum: https://discuss.pytorch.org/c/distributed/torchtitan/44