HuggingFace Llama3-8B Supervised Fine-tuning#

In this example, we will compile and finetune pre-trained HF Llama3-8B model on a single instance with the NxD Training library. The pre-trained Llama3-8B model serves as the foundation, and we will build upon this solid base by fine-tuning the model to adapt it to a specific task or dataset. The example has the following main sections:

Setting up the environment #

Install Dependencies #

Please follow this guide on how to install the latest Neuron packages: PyTorch Neuron Setup Guide.

Next, we will need to install NxD Training and its dependencies. Please see the following installation guide for installing NxD Training: NxD Training Installation Guide.

SFT-YAML Configuration Overview #

You can configuring a bunch of SFT-specific and model parameters for finetuning through the YAML file.

exp_manager:
    resume_from_checkpoint: /pretrained_ckpt

data:
    packing: True
    use_sft_data_module: True
    train_dir: /example_datasets/llama3_8b/training.jsonl
    validation_dir: /example_datasets/llama3_8b/validation.json
    dev_choose_samples: 2250
    seq_length: 4096
    tokenizer:
        type: /llama3_tokenizer

model:
    weight_init_only: True

exp_manager

resume_from_checkpoint

Manually set the checkpoint file (pretrained checkpoint) to load from

Type: str

Default: /pretrained_ckpt

Required: True (start with pretrained checkpoint)

data

packing

Appends multiple records in a single record until seq length supported by model, if false uses pad tokens to reach seq length. Setting it to True increases throughput but might impact accuracy.

Type: bool

Default: False

Required: False

use_sft_data_module

Use HF-SFT style SFT custom dataloader with HF style data file paths.

Type: bool

Default: True

Required: True

train_dir

SFT training data - jsonl or arrow file As for SFT we use HF style dataloader, we also use HF style data file paths

Type: str

Required: True

val_dir

SFT validation data - jsonl or arrow file As for SFT we use HF style dataloader, we also use HF style data file paths

Type: str

Required: True

dev_choose_samples

if set, will use those many number of records from the head of the dataset instead of using all. Set to null to use full dataset

Type: integer

Default: null

Required: False

seq_length

Set sequence length for the training job

Type: integer

Required: True

tokenizer
type

Set tokenizer path/type

Type: str

Default: /llama3_tokenizer

Required: True

model

weight_init_only

Load only model states and ignore the optim states from ckpt directory

Type: bool

Default: True

Download the dataset #

This tutorial makes use of a preprocessed version of databricks-dolly instruction-following dataset that is stored in S3. The dataset can be downloaded to your cluster or instance by running the following commands on the head node or your trn1 instance:

export DATA_DIR=~/examples_datasets/llama3_8b
mkdir -p ${DATA_DIR} && cd ${DATA_DIR}
aws s3 cp s3://neuron-s3/training_datasets/llama/sft/training.jsonl .  --no-sign-request
aws s3 cp s3://neuron-s3/training_datasets/llama/sft/validation.jsonl .  --no-sign-request

Download pretrained model checkpoint and tokenizer #

In this tutorial, we will use a pretrained Llama3-8B checkpoint from the original repository. Follow the steps to download tokenizer and model checkpoint from the pretraining stage: https://llama.meta.com/llama-downloads/

Create a folder /llama3_tokenizer and copy the tokenizer contents to it.

Modify the following paths in YAML file based on your specific directory configuration:

model.model_config
exp_manager.resume_from_checkpoint
tokenizer.type
train_dir and val_dir

You can use your custom model, pretrained checkpoint and tokenizer by modifying hf_llama3_8B_SFT_config.yaml file.

Checkpoint Conversion #

Follow this Checkpoint Conversion Guide to convert the HF-style Llama3-8B checkpoint to NxDT supported format and store it in /pretrained_ckpt/ directory. Modify the exp_manager.resume_from_checkpoint path to the pretrained checkpoint path.

Pre-compile the model #

By default, PyTorch Neuron uses a just in time (JIT) compilation flow that sequentially compiles all of the neural network compute graphs as they are encountered during a training job. The compiled graphs are cached in a local compiler cache so that subsequent training jobs can leverage the compiled graphs and avoid compilation (so long as the graph signatures and Neuron version have not changed).

An alternative to the JIT flow is to use the included neuron_parallel_compile command to perform ahead of time (AOT) compilation. In the AOT compilation flow, the compute graphs are first identified and extracted during a short simulated training run, and the extracted graphs are then compiled and cached using parallel compilation, which is considerably faster than the JIT flow.

First, ensure that you are using the proper config file in the conf/ directory. In the train.sh file, ensure that the CONF_FILE variable is properly set to the config for the model you want to use. In our case, it will be hf_llama3_8B_SFT_config. The default config here is a 8B parameter model, but users can also add their own conf/*.yaml files and run different configs and hyperparameters if desired. Please see Config Overview for examples and usage for the .yaml config files.

Next, run the following commands to launch an AOT pre-compilation job on your instance:

cd ~/neuronx-distributed-training/examples
export COMPILE=1
./train.sh

The compile output and logs will be shown directly in the terminal and you will see logs similar to this:

2024-08-11 23:04:08.000738: INFO ||PARALLEL_COMPILE||: Total graphs: 22
2024-08-11 23:04:08.000738: INFO ||PARALLEL_COMPILE||: Total successful compilations: 22
2024-08-11 23:04:08.000738: INFO ||PARALLEL_COMPILE||: Total failed compilations: 0

Then, you know your compilation has successfully completed.

Note

The number of graphs will differ based on package versions, models, and other factors. This is just an example.

Training the model #

The fine-tuning job is launched almost exactly in the same way as the compile job. We now turn off the COMPILE environment variable and run the same training script to start pre-training.

On a single instance:

export COMPILE=0
./train.sh

Once the model is loaded onto the Trainium accelerators and training has commenced, you will begin to see output indicating the job progress:

Example:

Epoch 0:   0%|          | 189/301501 [59:12<1573:03:24, 18.79s/it, loss=7.75, v_num=3-16, reduced_train_loss=7.560, global_step=188.0, consumed_samples=24064.0]
Epoch 0:   0%|          | 190/301501 [59:30<1572:41:13, 18.79s/it, loss=7.74, v_num=3-16, reduced_train_loss=7.560, global_step=189.0, consumed_samples=24192.0]
Epoch 0:   0%|          | 191/301501 [59:48<1572:21:28, 18.79s/it, loss=7.73, v_num=3-16, reduced_train_loss=7.910, global_step=190.0, consumed_samples=24320.0]

Monitoring Training #

Tensorboard monitoring #

In addition to the text-based job monitoring described in the previous section, you can also use standard tools such as TensorBoard to monitor training job progress. To view an ongoing training job in TensorBoard, you first need to identify the experiment directory associated with your ongoing job. This will typically be the most recently created directory under ~/neuronx-distributed-training/examples/nemo_experiments/hf_llama3_8B/. Once you have identifed the directory, cd into it, and then launch TensorBoard:

cd ~/neuronx-distributed-training/examples/nemo_experiments/hf_llama3_8B/
tensorboard --logdir ./

With TensorBoard running, you can then view the TensorBoard dashboard by browsing to http://localhost:6006 on your local machine. If you cannot access TensorBoard at this address, please make sure that you have port-forwarded TCP port 6006 when SSH’ing into the head node,

ssh -i YOUR_KEY.pem ubuntu@HEAD_NODE_IP_ADDRESS -L 6006:127.0.0.1:6006

neuron-top / neuron-monitor / neuron-ls #

The neuron-top tool can be used to view useful information about NeuronCore utilization, vCPU and RAM utilization, and loaded graphs on a per-node basis. To use neuron-top during on ongoing training job, run neuron-top:

ssh compute1-dy-queue1-i1-1  # to determine which compute nodes are in use, run the squeue command
neuron-top

Similarly, once you are logged into one of the active compute nodes, you can also use other Neuron tools such as neuron-monitor and neuron-ls to capture performance and utilization statistics and to understand NeuronCore allocation.

Troubleshooting Guide #

For issues with NxD Training, please see: NxD Training Known Issues

HuggingFace Llama3-8B Supervised Fine-tuning

Contents

HuggingFace Llama3-8B Supervised Fine-tuning#