Pré-entraînement distribué d'un modèle LLM sur TorchTitan
/SKILLPermet le pré-entraînement distribué de modèles de langage (LLM) natifs de PyTorch à l'aide de Torchtitan, avec un parallélisme 4D (FSDP2, TP, PP, CP).
name: distributed-llm-pretraining-torchtitan
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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Model Architecture, Distributed Training, TorchTitan, FSDP2, Tensor Parallel, Pipeline Parallel, Context Parallel, Float8, Llama, Pretraining]
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.txtDownload 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.shCommon 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 checkpointStep 1: Download tokenizer
python scripts/download_hf_assets.py \
--repo_id meta-llama/Llama-3.1-8B \
--assets tokenizer \
--hf_token=YOUR_HF_TOKENStep 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 = 500Step 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.tomlStep 4: Monitor and checkpoint
TensorBoard logs are saved to ./outputs/tb/:
tensorboard --logdir ./outputs/tbWorkflow 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 checkpointStep 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 sequencesStep 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.tomlStep 3: Submit job
sbatch multinode_trainer.slurmStep 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 compileStep 1: Install torchao
USE_CPP=0 pip install git+https://github.com/pytorch/ao.gitStep 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.enableWorkflow 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**Ste
