Expected Performance

Once the training job has finished successfully it's performance measurement metric is it's training throughput which is based on time it took to complete each training step.

The example below is taken from the end of the output log file - see $STAGE_PATH/results/$GSW_VERSION/$DTYPE/175b/$JOB_TOTAL_GPUS/*.out, where NUM_NODES=16, the training step time was measured as 5.9 seconds during step number 50.

grep train_step_timing results/fp8/175b/16/*.out
Epoch 0: : 100%|██████████| 50/50 [31:51<00:00, reduced_train_loss=6.130, global_step=50.0, consumed_samples=76800.0, train_step_timing in s=5.900, val_loss=6.250]

Since the performance fluctuates significantly at the beginning, we are using the last training step timing to obtain throughput value.

To obtain throughput as a tokens per second measurement, follow this formula:

(sequence length) * (global batch size) / (training_step_timing) = (throughput in tokens per second)

E.g. 2048 * 256 / 5.80 = 90394.48

To calculate time to train estimate:

 (total tokens) / (throughput in tokens per second) / (number of seconds in a day) = (time to train in days) 

E.g. 1e12 / 90394.48 / 86400 = 128.04 days

To calculate the model flops utilization (MFU):

MFU = (global batch size) * (model flops) / (training step time) / (number of GPUs) / (peak GPU FLOPS)

The peak theoretical throughput for H100 FP8 is 1979 TFLOPS and for H100 BF16 is 989 TFLOPS.

The model flops for GPT 3 175b for GBS=1 is 2.20E+15. Calculation shown here.

E.g. GPT 3 175b FP8 on 128x H100 GPUs (GBS=256)

peak FLOPS for H100 = 1979 TFLOPS
training step time = 5.90 s
model flops = 2.20E+15

MFU = 256 * 2.20E+15 / 5.90 / 128 / 1979E+12 = 37.68%

Prepare Environment

Create a staging area by running the setup.sh script. The script saves the container image from the registry in the $STAGE_PATH folder and copies the NeMo Launcher code from the container to the staging directory.

# Set the path where all artifacts will be downloaded
export STAGE_PATH=<path to your shared file system folder> (e.g. /lustre/myproject/nemo)
# Set the Slurm partition to launch against
export SLURM_PARTITION=<partition>
# Set the Slurm account to launch against
export SLURM_ACCOUNT=<account>
# Set the number of GPUs per node according to Slurm's gres, this is usually 8 or "" - https://slurm.schedmd.com/gres.html
export SLURM_GPUS_PER_NODE=""

# Run the setup
bash ./setup.sh

Note: Slurm parameters might not be applicable to all environments. Please consult with your system administrator and update or remove parameters as needed.

Download benchmark

Note, that this step requires that you have NGC CLI tooling installed on your slurm login node. If you don't have it installed follow these instructions:

1. Click the top-right "Download" button and select "CLI". This will copy the download command into clipboard.
2. From your terminal on login node, paste the download command and execute it.
3. Extract the downloaded benchmark zip containing required scripts and configuration files.

Prepare Dataset

Pre-training a GPT-3 model requires a text-based dataset to be downloaded and pre-processed for the NeMo Framework to ingest the data optimally. The Pile is often used as the dataset for pre-training models. The NeMo Framework contains helper scripts to download and pre-process the dataset. The following steps outline how to download and pre-process the dataset on DGX Cloud with an explanation of key points after.

Run the generate_dataset.sh script. The script launches several Slurm jobs that will download the dataset from The Pile, pre-process it and save it in a form suitable for subsequent training. The resulting dataset files will be saved under the $STAGE_PATH/gpt3-dataset folder. The dataset creation may use up to 250GB. Make sure you have sufficient disk space available.

bash ./generate_dataset.sh

If the dataset generation step was successful there should be 4 idx and 4 bin files in the $STAGE_PATH/gpt3-dataset folder.

my-gpt3_00_text_document.bin
my-gpt3_00_text_document.idx
my-gpt3_01_text_document.bin
my-gpt3_01_text_document.idx
my-gpt3_02_text_document.bin
my-gpt3_02_text_document.idx
my-gpt3_03_text_document.bin
my-gpt3_03_text_document.idx

You can run check_dataset.sh script to perform initial checks of the generated dataset state:

bash ./check_dataset.sh
Dataset generation completed successfully at: <$STAGE_PATH/gpt3-dataset>

If that is not the case, check the log files under: $STAGE_PATH/results.data_preparation

Run Training

Once the environment has been prepared, it is time to train a model. The NeMo Framework contains many predefined configuration files for various models including the 175 billion parameter GPT-3 model. This section will demonstrate how to initiate training on the model.

NeMo uses the Hydra framework to process command line arguments and the base config in the gpt3_175b_hydra.yaml file and passes them down as hyper parameters to a multi-node job performing the training.

Run the launch.sh script to start NeMo Megatron 175b model training. Minimum required number of nodes is 16 (or 128 GPUs). The training will run for the first 50 steps and will stop afterwards. Log files and results will be located under $STAGE_PATH/results/$GSW_VERSION/$DTYPE/175b/$JOB_TOTAL_GPUS folder.

Below is a command template for launching NeMo Megatron model training.

DTYPE=<fp8/bf16> sbatch -A ${SLURM_ACCOUNT} -p ${SLURM_PARTITION} -N ${NUM_NODES} ./launch.sh

Where:

• DTYPE is a required environment variable.
  • DTYPE can be either fp8 or bf16.
• NUM_NODES can be calculate by N_GPUS / N_GPUS_PER_NODE, N_GPUS_PER_NODE is 8 for DGX H100, therefore for 256 GPUs scale, NUM_NODES should be 256 / 8 = 32.
• -A and -p parameters are not applicable to all environments. Please consult with your system administrator and update or remove parameters as needed.

Note: it might be necessary to pass --gres=gpu:8 to sbatch for certain clusters on encountering errors like GPU not found. See https://slurm.schedmd.com/gres.html

It is important to maintain these values for model parallelism settings in order to accurately assess performance results for completed jobs against expected baseline, which can be seen in the gpt3_175b_hydra.yaml:

• training.model.tensor_model_parallel_size=4
• training.model.pipeline_model_parallel_size=8
• training.model.micro_batch_size=1
• training.model.virtual_pipeline_model_parallel_size=12

Global batch size ( training.model.global_batch_size) value should be set to <number of nodes> * 16. E.g., 16 * 16 = 256 (in the example above).

Notes

model flops = (sequence length) * ((attention flops) + (mlp flops) + (embedding flops))

model flops breakdown:
    attention flops = (24 * (number of layers) * (hidden size)^2) + (12 * (number of layers) * (hidden size) * (sequence length))
    mlp flops = 48 * (number of layers) * (hidden size)^2
    embedding flops = 6 * (vocab size) * (hidden size)

GPT 3 175b calculation:
    sequence length = 2048
    attention flops = 24 * 96 * 12288^2 + 12 * 96 * 12288 * 2048 = 376,883,380,224
    mlp flops = 48 * 96 * 12288^2 = 695,784,701,952
    embedding flops = 6 * 51200 * 12288 = 3,774,873,600

    model flops = 2048 * (376,883,380,224 + 695,784,701,952 + 3,774,873,600) = 2.20E+15