distributed-llm-pretraining-torchtitan
This PyTorch-native distributed LLM pretraining platform implements 4D parallelism (FSDP2, tensor, pipeline, and context parallelism) for pretraining large language models like Llama 3.1 and DeepSeek V3 across 8 to 512+ GPUs. Use it when scaling LLM pretraining with advanced techniques including Float8 precision, torch.compile optimization, and distributed checkpointing to achieve significant speedups on modern hardware.
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs /tmp/distributed-llm-pretraining-torchtitan && cp -r /tmp/distributed-llm-pretraining-torchtitan/01-model-architecture/torchtitan ~/.claude/skills/distributed-llm-pretraining-torchtitanSKILL.md
# 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**: ```bash # 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**: ```bash # 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**: ```bash 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** ```bash 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: ```toml # 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** ```bash # 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/`: ```bash 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): ```toml [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** ```bash #!/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** ```bash 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** ```bash USE_CPP=0 pip install git+https://github.com/pytorch/ao.git ``` **Step 2: Configure Float8** Add to your TOML config: ```toml [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** ```bash 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: ```bash 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** ```toml [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** ```bash # 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: ```toml [activation_checkpoint
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