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:
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 24.11 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 24.11 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 |
Time to train 1T tokens in days | 279 | 142 | 72 | 37 | 19 |
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)
# Set the Slurm partition to launch against
export SLURM_PARTITION="batch"
# Set the Slurm account to launch against
export SLURM_ACCOUNT="account_name"
# number of GPUs per node. For H100: 8, For GH200: 2
export SLURM_GPUS_PER_NODE=8
# Set the number of GPUs per node according to Slurm's gres, this is usually 8 or null - https://slurm.schedmd.com/gres.html
export SLURM_GPUS_PER_NODE=null
# Run the setup
bash ./setup.sh
Since Nemotron training only uses synthetic datasets, this step is omitted.
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 ${SLURM_ACCOUNT} -p ${SLURM_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 / SLURM_GPUS_PER_NODE
, SLURM_GPUS_PER_NODE
is 8 for DGX H100, therefore for 128 GPUs scale, NUM_NODES
should be 128 / 8 = 16
.Note: that 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:
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)
.
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