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distributed-llm-pretraining-torchtitan

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Updated 10-21 16:24

About this skill

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

# Or explicitly with torchrun
torchrun --nproc_per_node=8 \
  -m torchtitan.train \
  --job.config_file ./llama3_8b_custom.toml

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 \
  --job.config_file ./llama3_70b.toml

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

Add to your TOML config:

[model]
converters = ["quantize.linear.float8"]

[quantize.linear.float8]
enable_fsdp_float8_all_gather = true
precompute_float8_dynamic_scale_for_fsdp = true
filter_fqns = ["output"]  # Exclude output layer

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

Step 3: Launch with compile

CONFIG_FILE="./llama3_8b.toml" ./run_train.sh \
  --model.converters="quantize.linear.float8" \
  --quantize.linear.float8.enable_fsdp_float8_all_gather \
  --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 CONFIG_FILE=./llama3_405b.toml ./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 \
  --job.config_file ./llama3_405b.toml

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:

[quantize.linear.float8]
filter_fqns = ["attention.wk", "attention.wv", "output", "auto_filter_small_kn"]

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

Agent operating procedure

  1. Check the environment. Check GPU type, memory and driver/CUDA versions (nvidia-smi), framework versions, and dataset location and size.
  2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
  3. Run a small version first. Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.
  4. Execute the full task using the instructions and references above.
  5. Validate the result. Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.
  6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.
If this happens Do this
CUDA out-of-memory Reduce batch size, enable gradient accumulation/checkpointing or mixed precision, or shard the model.
Loss is NaN or diverges Lower the learning rate, check data for invalid values, and enable gradient clipping.
A function, flag or endpoint in these instructions is missing in the installed version Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous Ask the user, or state the assumption explicitly before running.

Integrity rules

  • Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
  • Never claim training results without logs; estimate compute cost before launching large jobs.
  • Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
  • Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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