Overview

This recipe contains information and scripts to produce performance results for the Nemotron 4 340B training workload. The scripts help perform environment setup and launch benchmark jobs.

This variant of the workload is best-suited for GPU clusters with:

• At least 128 GPUs with at least 80 GB memory each. Training of this 340-billion parameter variant of the workload will not fit on fewer GPUs with less memory.
• This workload supports BF16 or FP8 precision. FP8 is only supported by H100 GPUs. BF16 recipes are suitable for both A100 and H100 GPUs.

Expected Performance

Performance for Nemotron 4 training is measured by seconds per iteration, or in other words seconds per training step. This metric is logged for every training step in a .out file which is generated inside of the $STAGE_PATH/results/$GSW_VERSION/$DTYPE/340b/$JOB_TOTAL_GPUS folder.

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

grep train_step_timing results/*.out
Epoch 0: : 100%|██████████| 100/100 [07:57<00:00, reduced_train_loss=7.310, global_step=99.00, consumed_samples=25600.0, train_step_timing in s=3.590]

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. 4096 * 256 / 3.59 = 292082

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 / 292082 / 86400 = 39.6 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 NeMoTron4 340b for GBS=1 is 1.01e16. Calculation shown here.

E.g. NeMotron4 340b BF16 on 128x H100 GPUs (GBS=32)

peak FLOPS for H100 BF16 = 989 TFLOPS
training step time = 4.77 s
model flops = 1.01e16

MFU = 32 * 1.01e16 / 4.77 / 128 / 989e+12 = 53.52%

Nemotron4 340b BF16 (TP=8, PP=8, MBS=1, GA=16, VP=12)	Throughput on 128x H100 GPUs (GBS=32)	Throughput on 256x H100 GPUs (GBS=64)	Throughput on 512x H100 GPUs (GBS=128)	Throughput on 1024x H100 GPUs (GBS=256)	Throughput on 2048x H100 GPUs (GBS=512)
Training step time (seconds per step)	4.61	4.67	4.7	4.73	4.84
Throughput in tokens per second	28432	56134	111551	221686	433296
Model flops utilization	55.38%	54.67%	54.32%	53.98%	52.75%
Time to train 1T tokens in days	407	206	104	52	27

Nemotron4 340b FP8 (TP=8, PP=8, MBS=1, GA=16, VP=12)	Throughput on 128x H100 GPUs (GBS=32)	Throughput on 256x H100 GPUs (GBS=64)	Throughput on 512x H100 GPUs (GBS=128)	Throughput on 1024x H100 GPUs (GBS=256)	Throughput on 2048x H100 GPUs (GBS=512)
Training step time (seconds per step)	3.16	3.21	3.26	3.34	3.49
Throughput in tokens per second	41478	81665	160825	313945	600903
Model flops utilization	40.38%	39.75%	39.14%	38.20%	36.56%
Time to train 1T tokens in days	279	142	72	37	19

Prepare Environment

Slurm

We reference a number of Slurm commands and parameters in this document. A brief summary is included below. It's important to note these are a guide and might not be applicable to all environments. Please consult with your system administrator for the parameters that are specific to your system.

Common parameters:

• SBATCH_PARTITION or -p - Partition (or queue) to use.
• SBATCH_ACCOUNT or -A - Slurm account to associate with your job, different from your user. Meant for accounting purposes.
• SBATCH_GPUS_PER_NODE or --gres=gpu:<num gpus> - If your cluster is configured with GRES this should be set to all GPUs in a node. Ignore if not configured.
  • Encountering errors such as 'GPUs not found' or 'Cannot submit to this partition without GPU resources' means this setting is required.

These parameters can be set either by exporting the environment variable or using the corresponding sbatch flag.

Workload Setup

Create a staging area by running the attached setup.sh. The script converts the docker image from nvcr.io/nvidia/nemo:24.09 to the nvidia+nemo+24.09.sqsh file under the $STAGE_PATH folder and copies NeMo Launcher code from the container.

# Set the path where all artifacts will be downloaded
export STAGE_PATH=<path to your shared file system folder> (e.g. /lustre/myproject/nemo)

# Run the setup
sbatch -A ${SBATCH_ACCOUNT} -p ${SBATCH_PARTITION} -N 1 ./setup.sh

Check the corresponding slurm-<job_id>.out file for status information.

Important: STAGE_PATH used in this step must be used when running the workload.

Prepare Dataset

Since Nemotron training only uses synthetic datasets, this step is omitted.

Run Training

Once the environment has been prepared, it is time to train a model. NeMo Framework contains many predefined configuration files for various models including the 340 billion parameter Nemotron 4 model. This section will demonstrate how to initiate training the model. You can see the default configuration for Nemotron 340b in the NeMo-Framework-Launcher Github repository. We will modify some of these parameters with our launch command.

NeMo Launcher is using the Hydra framework to process command line arguments and pass them down as hyper parameters to a multi-node job performing the training.

The training will run for the first 50 steps and will stop afterwards. Log files and results will be located under the $STAGE_PATH/results/$GSW_VERSION/$DTYPE/340b/$JOB_TOTAL_GPUS folder.

Below is a slurm command template for launching Nemotron 4 340b model training.

DTYPE=<fp8/bf16> MODEL_SIZE=340b sbatch -A ${SBATCH_ACCOUNT} -p ${SBATCH_PARTITION} -N ${NUM_NODES} $STAGE_PATH/launch.sh

Where:

• DTYPE and MODEL_SIZE are required environment variables.
  • DTYPE can be either fp8 or bf16.
  • MODEL_SIZE should be 340b in this case.
• NUM_NODES can be calculate by N_GPUS / N_GPUS_PER_NODE, N_GPUS_PER_NODE is 8 for DGX H100, therefore for 128 GPUs scale, NUM_NODES should be 128 / 8 = 16.
• Slurm Settings for more information on Slurm parameters.

It is important to maintain these values for model parallelism settings in order to accurately assess performance results for completed jobs against expected baseline:

• training.model.tensor_model_parallel_size=8
• 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> * 2. E.g., 128 * 2 = 256 (in the example above).

Profiling

We have two profiling methods supported: Nsight, and NCCL Trace.

Due to overhead while profiling: the results generated with these settings is not valid for comparison. 'Performance' and 'Profiling' runs should be done separately.

Note: Profiling and NCCL Trace are currently mutually exclusive.

Run Nsight Profiling

Nsight Systems is included in our containers. To enable profiling with Nsight Systems set variable ENABLE_PROFILE=true when submitting your job.

In order to view the resulting profiles, ensure you have the latest version of Nsight Systems installed. For more information visit: Nsight Systems

Default Profiling Settings:

• MPI Ranks: 0-8
• Job Steps: 20-30
• Output Location: .nsys-rep files are saved in the nsys folder within the existing results directory.
• Filename format: ${MODEL}-${MODEL_SIZE}-${DTYPE}_${NUM_GPUS}g_${SLURM_JOB_ID}_${SLURM_NODEID}_${SLURM_LOCALID}.nsys-rep

Example command:

ENABLE_PROFILE=true DTYPE=<fp8/bf16> MODEL_SIZE=340b sbatch -A ${SBATCH_ACCOUNT} -p ${SBATCH_PARTITION} -N ${NUM_NODES} ./launch.sh

Customizing profiling behavior:

• Specify job steps to profile:
  • RUN_CONF_PROFILE_START_STEP: start profiling on this job step. Default: 20
  • RUN_CONF_PROFILE_STOP_STEP: stop profiling on this job step. Default: 30
• Select MPI ranks to profile:
  • RUN_CONF_PROFILE_RANKS: Comma-separated list of MPI ranks to profile. Example: "0,1,2,3" Default: "0,1,2,3,4,5,7"
• Enable GPU device metrics capture:
  • RUN_CONF_PROFILE_GPU_METRICS: boolean, set to 'true' to capture device metrics.
  • Default: false
  • Note: Additional system configuration is required for GPU device metrics to work.
• Enable CPU metrics capture:
  • RUN_CONF_PROFILE_CPU: boolean, set to 'true' to capture CPU metrics.
  • Default: false

Example customized profiling command:

ENABLE_PROFILE=true RUN_CONF_PROFILE_GPU_METRICS=true RUN_CONF_PROFILE_RANKS="0" DTYPE=<fp8/bf16> MODEL_SIZE=340b sbatch -A ${SBATCH_ACCOUNT} -p ${SBATCH_PARTITION} -N ${NUM_NODES} ./launch.sh

Troubleshooting:

If you encounter issues, try the defaults ENABLE_PROFILE=true first as these should be broadly applicable to most systems.

Viewing results

In order to view the profile traces (*.nsys-rep files) interactively:

• Install the latest Nsight Systems client on your preferred system
• Copy the generated .nsys-rep files to a folder on your preferred system. E.g., /home/nsight-traces/
• Open Nsight Systems client, then click "File | Open" and select one or more .nsys-rep files from /home/nsight-systems folder. For more details, see Reading Your Report in GUI guide.
• Once loaded you can analyze the workload behavior to learn about any performance bottlenecks associated with the job run.

Since most of the benchmarking jobs run on multiple GPUs, there will be multiple .nsys-rep files generated for each run. Multi-Report Analysis Guide will be very helpful to automate the analysis and get to results quicker by using Nsight recipes.

See these tutorials to get a quick start if you are new to Nsight profiling.

Run NCCL Trace

NCCL traces provide a breakdown of the communication pattern outlining both the type of NCCL calls being made and their message sizes.

To collect NCCL Trace information, set variable ENABLE_NCCL_TRACE=true when submitting your job.

Defaults:

• File Size: NCCL Trace generates large files, therefore profiling is limited to the first 10 steps.
• Output Location: Trace files are saved to a separate directory with nccl-trace appended to the version string.
• Output Directory: $STAGE_PATH/results/$GSW_VERSION-nccl-trace/$DTYPE/${MODEL_SIZE}/$JOB_TOTAL_GPUS

Example command:

ENABLE_NCCL_TRACE=true DTYPE=<fp8/bf16> MODEL_SIZE=340b sbatch -A ${SBATCH_ACCOUNT} -p ${SBATCH_PARTITION} -N ${NUM_NODES} ./launch.sh

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)

Nemotron4 340b calculation:
    sequence length = 4096
    number of layers = 96
    hidden size = 18432
    vocab size = 256000 
    attention flops = 24 * 96 * 18432^2 + 12 * 96 * 18432 * 4096 = 869730877440
    mlp flops = 48 * 96 * 18432^2 = 1565515579392
    embedding flops = 6 * 256000 * 18432 = 28311552000

    model flops = 4096 * (869730877440 + 1565515579392 + 28311552000) = 1.01e16