# Habana MLPerf™ inference submission This directory provides instructions to reproduce Habana's results for MLPerf™ inference submission.\ MLPerf™ is a trademark and service mark of MLCommons Association in the United States and other countries.\ All rights reserved. Unauthorized use is strictly prohibited. - [Habana MLPerf™ inference submission](#habana-mlperf-inference-submission) - [Setup](#setup) - [Prepare MLPerf Directory](#prepare-mlperf-directory) - [Build and Deploy HabanaLabs Container](#build-and-deploy-habanalabs-container) - [Download Checkpoint](#download-checkpoint) - [Download Dataset](#download-dataset) - [Reproduce Results](#reproduce-results) - [99 and 99.9 Accuracy](#99-and-999-accuracy) - [Get Started](#get-started) - [Generate Results](#generate-results) - [Performance Optimization with FP8 Flow](#performance-optimization-with-fp8-flow) - [Environment Variables](#environment-variables) - [Supported Configurations](#supported-configurations) - [Changelog](#changelog) ## Setup Please follow the instructions provided in the [Gaudi Installation Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html) to set up the environment. ### Prepare MLPerf Directory Perform the following: 1. Follow the instructions provided in the [Gaudi Installation Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html) to set up the environment including the `$PYTHON` environment variable. The guide will walk you through the process of setting up your system to run the benchmarks on Gaudi. 2. Clone Model-References repository and switch to the branch that matches your SynapseAI version. You can run the [`hl-smi`](https://docs.habana.ai/en/latest/Management_and_Monitoring/System_Management_Tools_Guide/System_Management_Tools.html#hl-smi-utility-options) utility to determine the SynapseAI version. ```bash export MLPERF_ROOT=/path/to/mlperf/root cd $MLPERF_ROOT git clone -b [SynapseAI version] https://github.com/HabanaAI/Model-References export MLPERF_DIR=$MLPERF_ROOT/Model-References/MLPERF3.1/Inference ``` ### Build and Deploy HabanaLabs Container To build MLPerf inference 3.1 container, perform the following: 1. Set the environment variables for the docker command. * To find a docker image, go to [gaudi-docker](https://vault.habana.ai/ui/repos/tree/General/gaudi-docker). * Open gaudi-docker directory, and select the folder that matches the SynapseAI version (determined by running [`hl-smi`](https://docs.habana.ai/en/latest/System_Management_Tools_Guide/System_Management_Tools.html#hl-smi-utility-options)). * Navigate to subdirectories, choose system and framework version. * Choose the docker build version. Most often 'latest' will be used. * Navigate to "Docker Info" tab and note "Title" string. * Set `DOCKER_IMAGE` to "Title" string with `vault.habana.ai/gaudi-docker/` prefix. See the examples below. * Example on PyTorch Container: ```bash # NOTE: The below is only an example value. Replace [SynapseAI version] and [PT version] to match your setup and Supported Configuration. export DOCKER_IMAGE=vault.habana.ai/gaudi-docker/[SynapseAI version]/ubuntu20.04/habanalabs/pytorch-installer-[PT Version]:latest ``` 2. Create `mlperf-habana container` by running the following command. ```bash docker run --privileged --security-opt seccomp=unconfined \ --name mlperf-habana -td \ -v /dev:/dev \ --device=/dev:/dev \ -v /sys/kernel/debug:/sys/kernel/debug \ -v /tmp:/tmp \ -v $MLPERF_DIR:/root/Habana/ \ --cap-add=sys_nice --cap-add=SYS_PTRACE \ --user root --workdir=/root --net=host \ --ulimit memlock=-1:-1 ${DOCKER_IMAGE} ``` 3. Start the docker. ```bash docker exec -it mlperf-habana bash ``` ### Download Checkpoint ```bash mkdir -p /mnt/weka/data/pytorch/ pushd /mnt/weka/data/pytorch/ wget https://cloud.mlcommons.org/index.php/s/QAZ2oM94MkFtbQx/download --output-document checkpoint.zip unzip -q checkpoint.zip && rm checkpoint.zip popd ``` ### Download Dataset ```bash pushd /root/Habana/code/gptj-99.9/gpt-j python download_cnndm.py cp data/cnn_eval.json /mnt/weka/data/pytorch/gpt-j/cnn_eval.json popd ``` ## Reproduce Results ### 99 and 99.9 Accuracy The same script was submitted for both 99 and 99.9 benchmarks - no additional improvements were made for low accuracy (99), and 99.9 results were used for 99 as well. ### Get Started Install the requirements and build the latest loadgen. ```bash cd /root/Habana/code source functions.sh build_mlperf_inference ``` ### Generate Results **To generate full submission results, run the following command:** ```bash build_mlperf_inference --output-dir --submission gptj-99.9-fp8 ``` The command produces results from accuracy and performance runs for both Offline and Server scenarios. Logs can be found under /output_dir/logs/model/, e.g. /results/logs/gptj-99.9-fp8/ **To generate results for Offline and Server scenarios separately, run the following commands:** ```bash build_mlperf_inference --output-dir --submission gptj-99.9-fp8_Offline ``` ```bash build_mlperf_inference --output-dir --submission gptj-99.9-fp8_Server ``` Logs can be found under /output_dir/logs/model/scenario/, e.g. /results/logs/gptj-99.9-fp8/Offline/ **To generate results for accuracy and performance separately, add ```--mode``` flag as in one of the following commands:** ```bash build_mlperf_inference --output-dir --submission gptj-99.9-fp8_Server --mode acc ``` ```bash build_mlperf_inference --output-dir --submission gptj-99.9-fp8_Offline --mode perf ``` Logs can be found under /output_dir/logs/model/scenario/mode/, e.g. /results/logs/gptj-99.9-fp8/Offline/accuracy/ ## Performance Optimization with FP8 Flow To optimize performance, we set heavy-performance ops to operate in fp8-143. All fp8 ops are working with a fixed fp8 exponent bias = 7 and no scaling is required. ### Environment Variables The following outlines custom ENV variables used in the GPT-J submission script: | Enviroment Variable | Effect | |------------------------------------------------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | PT_USE_FP8_143=1 | Sets PT backend fp8 flavor to fp8_143 | | UPDATE_MME_OUTPUT_PRECISION_FILTER="v_proj,matmul_av" | Allows the specified MME layer to output fp8 for performance optimization. | | SCALES_FILE_PATH=quantization/measurements/per_tensor_scales_gpt_j.json | Loads per-tensor scales required for fp8 quantization. If not provided, no scaling is applied. | | ENABLE_EXPERIMENTAL_FLAGS=true | Enables the above flags | ## Supported Configurations | Validated on | SynapseAI Version | Framework Version(s) | Mode | | :----------: | :---------------: | :------------------: | :------: | | Gaudi2 | 1.14.0 | PyTorch 2.1.1 | Inference | ## Changelog ### 1.13.0 - Published MLPerf™ inference 3.1 GPT-J script