HuggingFace Llama3-8B Pretraining

In this example, we will compile and train a HF Llama3-8B model on a single instance with the NxD Training (NxDT) library. The example has the following main sections:

Setting up the environment

Install Dependencies

Once you have launched a Trn1 instance, please follow this guide on how to install the latest Neuron packages: PyTorch Neuron Setup Guide.

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

Download the dataset

This tutorial makes use of a preprocessed and tokenized Wikicorpus dataset that is stored in S3. The dataset can be downloaded to your cluster or instance by running the following AWS CLI commands on the head node or your Trn1 instance:

export DATA_DIR=~/examples_datasets/
mkdir -p ${DATA_DIR} && cd ${DATA_DIR}
aws s3 cp s3://neuron-s3/training_datasets/llama3/wikicorpus_llama3_tokenized_8k .  --no-sign-request
aws s3 cp s3://neuron-s3/training_datasets/llama3/llama3_8B_config.json ~/config.json  --no-sign-request

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, clone the open-source neuronx-distributed-training library

git clone https://github.com/aws-neuron/neuronx-distributed-training
cd neuronx-distributed-training/examples

Now, 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_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:

export COMPILE=1
./train.sh

The compile output and logs will be shown directly in the terminal and you will see a message 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 pre-training job is launched almost exactly the same 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 NxDT, please see: NxDT Known Issues