distributed-llm-pretraining-torchtitan
GitHub基于PyTorch的原生分布式LLM预训练工具，支持4D并行及H100加速。适用于Llama 3.1等模型在8至512+ GPU上的大规模预训练、配置调优及多节点SLURM部署。
Trigger Scenarios
Install
npx skills add synthetic-sciences/openscience --skill distributed-llm-pretraining-torchtitan -g -y
SKILL.md
Frontmatter
{
"name": "distributed-llm-pretraining-torchtitan",
"tags": [
"Model Architecture",
"Distributed Training",
"TorchTitan",
"FSDP2",
"Tensor Parallel",
"Pipeline Parallel",
"Context Parallel",
"Float8",
"Llama",
"Pretraining"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "ml-training",
"description": "Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.",
"dependencies": [
"torch>=2.6.0",
"torchtitan>=0.2.0",
"torchao>=0.5.0"
]
}
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
- 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
Version History
- e9844a4 Current 2026-07-11 17:31
Dependencies
-
required
torch>=2.6.0 -
required
torchtitan>=0.2.0 -
required
torchao>=0.5.0
