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Fine-tuning with Multi GPU

This recipe steps you through how to finetune a Meta Llama 3 model on the text summarization task using the samsum dataset on multiple GPUs in a single or across multiple nodes.

Requirements

Ensure that you have installed the llama-recipes package (details).

We will also need 2 packages:

  1. PEFT to use parameter-efficient finetuning.
  2. FSDP which helps us parallelize the training over multiple GPUs. More details.

Note

The llama-recipes package will install PyTorch 2.0.1 version. In case you want to use FSDP with PEFT for multi GPU finetuning, please install the PyTorch nightlies (details)

INT8 quantization is not currently supported in FSDP

How to run it

Get access to a machine with multiple GPUs (in this case we tested with 4 A100 and A10s).

With FSDP + PEFT

Single-node Multi-GPU 
torchrun --nnodes 1 --nproc_per_node 4  finetuning.py --enable_fsdp --model_name /path_of_model_folder/8B --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model
Multi-node Multi-GPU Here we use a slurm script to schedule a job with slurm over multiple nodes. 
# Change the num nodes and GPU per nodes in the script before running.
sbatch ./multi_node.slurm

We use torchrun to spawn multiple processes for FSDP.

The args used in the command above are:

  • --enable_fsdp boolean flag to enable FSDP in the script
  • --use_peft boolean flag to enable PEFT methods in the script
  • --peft_method to specify the PEFT method, here we use lora other options are llama_adapter, prefix.

With only FSDP

If interested in running full parameter finetuning without making use of PEFT methods, please use the following command. Make sure to change the nproc_per_node to your available GPUs. This has been tested with BF16 on 8xA100, 40GB GPUs.

torchrun --nnodes 1 --nproc_per_node 8  finetuning.py --enable_fsdp --model_name /path_of_model_folder/8B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --use_fast_kernels

Using less CPU memory (FSDP on 70B model)

If you are running full parameter fine-tuning on the 70B model, you can enable low_cpu_fsdp mode as the following command. This option will load model on rank0 only before moving model to devices to construct FSDP. This can dramatically save cpu memory when loading large models like 70B (on a 8-gpu node, this reduces cpu memory from 2+T to 280G for 70B model). This has been tested with BF16 on 16xA100, 80GB GPUs.

torchrun --nnodes 1 --nproc_per_node 8 finetuning.py --enable_fsdp --low_cpu_fsdp --pure_bf16 --model_name /path_of_model_folder/70B --batch_size_training 1 --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned

Running with different datasets

Currently 3 open source datasets are supported that can be found in Datasets config file. You can also use your custom dataset (more info here).

  • grammar_dataset : use this notebook to pull and process the Jfleg and C4 200M datasets for grammar checking.

  • alpaca_dataset : to get this open source data please download the aplaca.json to dataset folder.

wget -P ../../src/llama_recipes/datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
  • samsum_dataset

To run with each of the datasets set the dataset flag in the command as shown below:

# grammer_dataset
torchrun --nnodes 1 --nproc_per_node 4  finetuning.py --enable_fsdp  --model_name /path_of_model_folder/8B --use_peft --peft_method lora --dataset grammar_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned  --pure_bf16 --output_dir Path/to/save/PEFT/model

# alpaca_dataset

torchrun --nnodes 1 --nproc_per_node 4  finetuning.py --enable_fsdp  --model_name /path_of_model_folder/8B --use_peft --peft_method lora --dataset alpaca_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --output_dir Path/to/save/PEFT/model


# samsum_dataset

torchrun --nnodes 1 --nproc_per_node 4  finetuning.py --enable_fsdp --model_name /path_of_model_folder/8B --use_peft --peft_method lora --dataset samsum_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --output_dir Path/to/save/PEFT/model

[TIP] Slow interconnect between nodes?

In case you are dealing with slower interconnect network between nodes, to reduce the communication overhead you can make use of --hsdp flag.

HSDP (Hybrid sharding Data Parallel) helps to define a hybrid sharding strategy where you can have FSDP within sharding_group_size which can be the minimum number of GPUs you can fit your model and DDP between the replicas of the model specified by replica_group_size.

This will require to set the Sharding strategy in fsdp config to ShardingStrategy.HYBRID_SHARD and specify two additional settings, sharding_group_size and replica_group_size where former specifies the sharding group size, number of GPUs that you model can fit into to form a replica of a model and latter specifies the replica group size, which is world_size/sharding_group_size.

torchrun --nnodes 4 --nproc_per_node 8 ./finetuning.py --enable_fsdp --low_cpu_fsdp --fsdp_config.pure_bf16 --model_name /path_of_model_folder/70B --batch_size_training 1 --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --hsdp --sharding_group_size n --replica_group_size world_size/n

FLOPS Counting and Pytorch Profiling

To help with benchmarking effort, we are adding the support for counting the FLOPS during the fine-tuning process. You can achieve this by setting --flop_counter when launching your single/multi GPU fine-tuning. Use --flop_counter_start to choose which step to count the FLOPS. It is recommended to allow a warm-up stage before using the FLOPS counter.

Similarly, you can set --use_profiler flag and pass a profiling output path using --profiler_dir to capture the profile traces of your model using PyTorch profiler. To get accurate profiling result, the pytorch profiler requires a warm-up stage and the current config is wait=1, warmup=2, active=3, thus the profiler will start the profiling after step 3 and will record the next 3 steps. Therefore, in order to use pytorch profiler, the --max-train-step has been greater than 6. The pytorch profiler would be helpful for debugging purposes. However, the --flop_counter and --use_profiler can not be used in the same time to ensure the measurement accuracy.