Skip to content

Latest commit

 

History

History
1580 lines (1152 loc) · 66 KB

scc23-mlperf-inference-bert.md

File metadata and controls

1580 lines (1152 loc) · 66 KB
Raw

[ Back to index ]

Tutorial to run and optimize MLPerf BERT inference benchmark at SCC'23

Click here to see the table of contents.

Introduction

This tutorial was prepared for the Student Cluster Competition'23 to explain how to run and optimize the MLPerf inference benchmark with BERT Large model variations across different software and hardware. You will need to obtain the best throughput for your system (samples per second) without degrading accuracy.

You will also learn how to use the MLCommons CM automation language that helps to modularize and automate (MLPerf) benchmarks using portable, technology-agnostic and reusable CM scripts. CM scripts are developed by the MLCommons task force on automation and reproducibility, the cTuning foundation and the community.

During this tutorial you will learn how to:

  • Install, understand and use the MLCommons CM automation language on your system.
  • Prepare the MLPerf BERT inference benchmark and make the first test run on a CPU using CM.
  • Obtain official results (accuracy and throughput) for MLPerf BERT question answering model in offline mode on a CPU or GPU of your choice.
  • Learn how to optimize this benchmark and submit your results to the SCC committee.

It should take less than an hour to complete this tutorial including 30 minutes to run the optimized version of this benchmark to completion. In the end, you should obtain a tarball (mlperf_submission.tar.gz) with the MLPerf-compatible results that you will submit to the SCC organizers to get points.

An interactive version of the short version of this tutorial is available at this Google Colab page.

Scoring

During SCC, you will first attempt to run a reference (unoptimized) Python implementation of the MLPerf inference benchmark with the BERT fp32 model, SQuAd v1.1 dataset, any ONNX runtime (MLPerf framework or backend) with CPU target (MLPerf device) and MLPerf loadgen library to get a minimal set of points.

After a successful run, you will be able to run optimized version of this benchmark on a GPU (Nvidia, AMD) or CPU (x64, Arm64), change ML frameworks, try different batch sizes, etc to get better performance than the reference implementation that will be converted in extra points.

Note that since not all vendor implementations of MLPerf inference benchmark are still equal and they mostly support 1-node benchmarking at this stage, teams will compete to get better throughput only between the same architecture type (CPU, Nvidia GPU, AMD GPU, etc) to get more points proportional to the MLPerf BERT inference throughput obtained on their systems.

Furthermore, if you improve existing implementation and/or provide support for new hardware (such as AMD GPU) add support for multi-node execution or improve MLPerf BERT models without dropping accuracy, and make all your improvements publicly available under Apache 2.0 license when submitting results to the SCC committee, you will get major bonus points for supporting the MLPerf community. For example, improving reference implementation or adding new hardware backend will give more points than just running official Nvidia implementation of the MLPerf inference benchmark.

After SCC, you are welcome to prepare an official submission to the next inference v4.0 round in February 2024 to get your results and the team name to the official MLCommons release similar to v3.1. (see the announcement of community MLPerf submissions from the cTuning foundation at HPC Wire).

Note that both MLPerf and CM automation are evolving projects. If you encounter issues or have questions, please submit them here and feel free to get in touch with the CM-MLPerf community via our public Discord server.

Artifacts to submit to the SCC committee

You will need to submit the following files with the reference (non-optimized) MLPerf BERT inference results to obtain the first (min) set of points:

  • mlperf_submission_short.tar.gz - automatically generated file with validated MLPerf results.
  • mlperf_submission_short_summary.json - automatically generated summary of MLPerf results.
  • mlperf_submission_short.run - CM commands to run MLPerf BERT inference benchmark saved to this file.
  • mlperf_submission_short.tstamps - execution timestamps before and after CM command saved to this file.
  • mlperf_submission_short.md - description of your platform and some highlights of the MLPerf benchmark execution.

You will need to submit the following files with the optimized MLPerf BERT inference results to obtain main points (including major bonus points for improving existing benchmark implementations and adding new hardware backends):

  • mlperf_submission_{N}.tar.gz - automatically generated file with validated MLPerf results.
  • mlperf_submission_{N}_summary.json - automatically generated summary of MLPerf results.
  • mlperf_submission_{N}.run - CM commands to run MLPerf BERT inference benchmark saved to this file.
  • mlperf_submission_{N}.tstamps - execution timestamps before and after CM command saved to this file.
  • mlperf_submission_{N}.md - your highlights, optimizations, improvements and extensions of the MLPerf BERT inference benchmark (new hardware backends, support for multi-node execution, batch size, quantization, etc). Note that you will need to provide a PR with open-source Apache 2.0 improvements to the MLCommons inference repo our our stable fork.

where N is your attempt number out of 5.

You can find sample artifacts in this shared drive.

SCC interview

You are encouraged to highlight and explain the obtained MLPerf BERT inference throughput on your system and describe any improvements and extensions to this benchmark (such as adding new hardware backend or supporting multi-node execution) useful for the community and MLCommons.

System preparation

Minimal system requirements to run unoptimized MLPerf BERT inference benchmark

  • CPU: 1 node (x86-64 or Arm64)
  • OS: we have tested this automation on Ubuntu 20.04, Ubuntu 22.04, Debian 10, Red Hat 9 and MacOS 13
  • Disk space: ~10GB
  • Python: 3.8+
  • All other dependencies (model, data set, benchmark, libraries and tools) should be detected or installed by CM

Extra system requirements for Nvidia GPU

  • GPU: Nvidia GPU with 8GB+ memory (official optimized backend) or AMD GPU via experimental backend from one of the teams
  • Disk space: ~ 30GB

MLCommons CM automation language

The MLCommons and cTuning foundation has developed an open-source and technology-agnostic Collective Mind automation language (MLCommons CM) to modularize AI/ML Systems and automate their benchmarking, optimization and design space exploration across continuously changing software, hardware and data.

CM is a small Python library with minimal dependencies and a unified CLI, Python API and human readable YAML/JSON inputs and meta descriptions. It is extended by the community via [portable CM scripts] that should run on any platform with any OS. Our goal is to provide a common, human-friendly and technology-agnostic interface to unify and automate all the steps to prepare, run and reproduce benchmarks and research projects across diverse ML models, datasets, frameworks, compilers and hardware.

We suggest you to check this simple tutorial to run modular image classification using CM to understand how CM works and see ACM TechTalk'21 and ACM REP'23 keynote to learn about our motivation and long-term vision.

CM installation

Follow this guide to install the MLCommons CM framework on your system.

After the installation, you should be able to access the CM command line as follows:

cm
cm {action} {automation} {artifact(s)} {--flags} @input.yaml @input.json
cm --version
1.5.3

Pull CM repository with cross-platform MLOps and DevOps scripts

Pull the stable version of the MLCommons repository with cross-platform CM scripts for modular benchmarking and optimization of AI/ML Systems:

cm pull repo mlcommons@ck --checkout=scc23

or using equivalent form with a full URL:

cm pull repo --url=https://github.com/mlcommons/ck --checkout=scc23

CM pulls all such repositories into the $HOME/CM directory to search for CM automations and portable scripts that can run on any OS (Linux, Windows, MacOS). You can find the location of a pulled repository as follows:

cm find repo mlcommons@ck
mlcommons@ck,a4705959af8e447a = /home/ubuntu/CM/repos/mlcommons@ck

Note that you can use the latest master version instead of the stable SCC'23 branch by removing --checkout flag - it should be safe since our goal is to keep the CM interface always unified and backward compatible while continuously improving the underlying CM scripts based on the feedback from the community when running them across diverse software and hardware.

You can update this repository at any time with the same command:

cm pull repo mlcommons@ck

Note that if something goes wrong, you can always start from scratch by simply removing $HOME/CM directory with all repositories completely:

rm -rf $HOME/CM

Test MLPerf BERT inference benchmark out-of-the-box

We suggest you to read this section but not run the CM script - in such case, you will be able to learn how to prepare the MLPerf BERT benchmark step-by-step using reusable CM automations in the next section.

You are now ready to run the reference (unoptimized) Python implementation of the MLPerf language benchmark with BERT model and ONNX backend using the modular CM-MLPerf pipeline.

Normally, you would need to go through this README.md and use Docker containers or prepare all the dependencies and environment variables manually.

Note that at this stage, you should normally be able to run the MLPerf BERT inference benchmark out-of-the-box using just one CM command that will automatically detect all the required dependencies and download and install the missing ones including benchmark sources, models, data sets, ML frameworks, libraries and tools. However, we suggest you run this command only at the end of this tutorial to get more details about how it works:

cm run script "app mlperf inference generic _python _bert-99 _onnxruntime _cpu _test" \
     --scenario=Offline \
     --mode=accuracy \
     --test_query_count=10 \
     --rerun \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --quiet

Note that you can use cmr alias instead of cm run script:

cmr "app mlperf inference generic _python _bert-99 _onnxruntime _cpu _test" \
     --scenario=Offline \
     --mode=accuracy \
     --test_query_count=10 \
     --rerun \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --quiet

You will see a long output that should contain the following line with accuracy:

{"exact_match": 70.0, "f1": 70.0}

  • --device=cuda and --device=rocm can be used to run the inference on Nvidia GPU and AMD GPUs respectively. The current reference implementation supports only one GPU instance for inference but this can be changed.

  • --adr.mlperf-implementation.tags=_repo.URL allows you to use your own fork of the MLPerf inference repo if you want to improve it. We use the cTuning fork to keep a stable version of the official MLPerf inference repository.

You can add -v flag to debug any CM script.

Prepare to run MLPerf step-by-step via CM

Install system dependencies for your platform

First, you need to install various system dependencies required by the MLPerf inference benchmark.

For this purpose, we have created a cross-platform CM script that will automatically install such dependencies based on your OS (Ubuntu, Debian, Red Hat, MacOS ...).

In this case, CM script serves simply as a wrapper with a unified and cross-platform interface for native scripts that you can find and extend here if some dependencies are missing on your machine - this is a collaborative way to make CM scripts portable and interoperable.

You can run this CM scripts as follows (note that you may be asked for a SUDO password on your platform):

cmr "get sys-utils-cm" --quiet

If you think that you have all system dependencies installed, you can run this script without --quiet flag and type "skip" in the script prompt.

Use CM to detect or install Python 3.8+

Since we use Python reference implementation of the MLPerf inference benchmark (unoptimized), we need to detect or install Python 3.8+ (MLPerf requirement).

You need to detect it using the following CM script:

cmr "get python" --version_min=3.8

You may see the output similar to

* cm run script "get python"
  - Searching for versions:  >= 3.8

    * /usr/bin/python3
      Detected version: 3.10.12

      Found artifact in /usr/bin/python3
      Detected version: 3.10.12

Note, that all artifacts (including the above scripts) in MLCommons CM are organized as a database of interconnected components. They can be found either by their user friendly tags (such as get,python) or aliases (get-python3) and unique identifiers (5b4e0237da074764). You can find this information in a CM meta description of this script.

If required Python is installed on your system, CM will detect it and cache related environment variables such as PATH, PYTHONPATH, etc. to be reused by other CM scripts. You can find an associated CM cache entry for your python as follows:

cm show cache --tags=get,python

or

cm show cache "get python"

You should see the output similar to

* Tags: get,get-python,get-python3,non-virtual,python,python3,script-artifact-d0b5dd74373f4a62,version-3.10.12
  Path: /home/ubuntu/CM/repos/local/cache/d7d9e170f0ac498f
  Version: 3.10.12

CM will automatically generate a unique CM ID (16 hexadecimal lowercase characters) on your system and cache produced artifacts and meta descriptions there.

For example, you can see all the environment variables produced by this CM script (that can be reused in your projects or other CM scripts) in the following JSON file:

cat `cm find cache "get python"`/cm-cached-state.json

If required Python is not detected, CM will automatically attempt to download and build it from sources using another cross-platform CM script "install-python-src". In the end, CM will also cache new binaries and related environment variables such as PATH, PYTHONPATH.

You can see the state of CM cache with all reusable artifacts and meta-descriptions as follows:

cm show cache

You can find the PATH to cached artifacts and reuse them in your own projects as follows:

cm find cache "get python"

Note that if you run the same script again, CM will automatically find and reuse the cached output:

cmr "get python" --version_min=3.8 --out=json

You can also clean CM cache and start from scratch as follows:

cm rm cache -f

Setup a virtual environment for Python

We also automated the very commonly used command to install virtual environment. You can create multiple virtual environments that will be automatically used by CM scripts as follows:

cmr "install python-venv" --name=mlperf
export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"

Now flag --adr.python.name=mlperf will be added to all CM scripts automatically forcing them to use this virtual environment. You can also delete a given virtual environment and start from scratch as follows:

cm rm cache "python name-mlperf" -f

You can test the virtual environment using CM script to print "Hello World" via detected python

cmr "python hello-world"

You should normally see that python is used from the CM cache:

* cm run script "python hello-world"
  * cm run script "detect os"
  * cm run script "get sys-utils-cm"
  * cm run script "get python3"
  * cm run script "print python-version"
    * cm run script "get python3"

CM_PYTHON_BIN = python3
CM_PYTHON_BIN_WITH_PATH = /home/ubuntu/CM/repos/local/cache/b9dafcf15d4045af/mlperf/bin/python3

Python 3.10.12
/home/ubuntu/CM/repos/local/cache/b9dafcf15d4045af/mlperf/bin/python3
Python 3.10.12

HELLO WORLD from Python

Compile MLPerf loadgen

You need to compile the MLPerf C++ loadgen library with Python bindings while forcing compiler dependency to GCC using the CM "get-mlperf-inference-loadgen" script:

cmr "get mlperf loadgen" --adr.compiler.tags=gcc --quiet

The --adr flag stands for "Add to all Dependencies Recursively" and will find all sub-dependencies on other CM scripts in the CM loadgen script with the "compiler" name and will append "gcc" tag to enforce detection and usage of GCC to build loadgen.

Download the SQuAD validation dataset

You can download the SQuAD v1.1 validation dataset using the CM "get-dataset-squad" script as follows:

cmr "get dataset squad original"

After installing this dataset via CM, you can reuse it in your own projects or other CM scripts (including MLPerf benchmarks). You can check the CM cache as follows (the unique ID of the CM cache entry will be different on your machine):

cm show cache "get dataset squad original"
* Tags: dataset,get,language-processing,original,script-artifact-6651c119c3ae49b3,squad,validation,version-1.1
  Path: /home/arjun/CM/repos/local/cache/e5ac8a524ba64d09
  Version: 1.1

Detect or install ONNX runtime for CPU

You can now detect or install the ONNX Python run-time on your system while targeting CPU using this generic CM script to install any Python package to the default or virtual Python installation using so-called CM script variations prefixed by _:

cmr "get generic-python-lib _onnxruntime"

If needed, you can install a specific version of ONNX runtime using flag --version:

cmr "get generic-python-lib _onnxruntime" --version=1.16.1

Download Bert-large model (FP32, ONNX format)

Download and cache this reference model in the ONNX format (float32) using the following CM script:

cmr "get ml-model language-processing bert-large _onnx"

It takes around ~1GB of disk space. You can find it in the CM cache as follows:

cm show cache --tags=get,ml-model,bert-large,_onnx
*Tags: bert,bert-large,bert-squad,get,language,language-processing,ml-model,raw,script-artifact-5e865dbdc65949d2,_amazon-s3,_fp32,_onnx
  Path: /home/arjun/CM/repos/local/cache/8727a38b72aa4b3f

Note that you will have a different CM UID consisting of 16 hexadecimal lowercase characters.

You can find downloaded model as follows:

ls `cm find cache "download ml-model bert-large"` -l

...
 1340711828 Nov  5 14:31 model.onnx
...

Pull MLPerf inference sources with reference benchmark implementations

You should now download and cache the MLPerf inference sources using the following command:

cmr "get mlperf inference src _repo.https://github.com/ctuning/inference _branch.scc23" \
     --version=custom

We use the cTuning fork of the MLPerf inference repo to make sure that there are no last-minute changes during SCC.

You can use your own fork if you want to improve/optimize benchmark implementations.

Run reference MLPerf inference benchmark with ONNX run-time

Run short reference MLPerf inference benchmark to measure accuracy (offline scenario)

You are now ready to run the reference (unoptimized) Python implementation of the MLPerf language benchmark with BERT model, ONNX backend and Offline scenario while measuring accuracy.

Normally, you would need to go through this README.md and use Docker containers or prepare all the dependencies and environment variables manually.

The CM "app-mlperf-inference" script allows you to run this benchmark in a native environment as follows (short test run with 10 samples):

cmr "app mlperf inference generic _python _bert-99 _onnxruntime _cpu _test" \
     --scenario=Offline \
     --mode=accuracy \
     --test_query_count=10 \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --adr.compiler.tags=gcc \
     --quiet \
     --rerun

This CM script will automatically find or install all dependencies described in its CM meta description, aggregate all environment variables, preprocess all files and assemble the MLPerf benchmark CMD. After running the benchmark, it calls the MLPerf accuracy script to evaluate the accuracy of the results.

It will take a few minutes to run it and you should see the following accuracy:

{"exact_match": 70.0, "f1": 70.0}
Reading examples...
No cached features at 'eval_features.pickle'... converting from examples...
Creating tokenizer...
Converting examples to features...
Caching features at 'eval_features.pickle'...
Loading LoadGen logs...
Post-processing predictions..

Congratulations, you can now play with this benchmark using the unified CM interface!

Note that even if did not install all the above dependencies manually, the below command will automatically install all the necessary dependencies.

Run short MLPerf inference benchmark to measure performance (offline scenario)

Let's run the MLPerf language processing benchmark while measuring performance in offline scenario (samples per second):

cmr "app mlperf inference generic _python _bert-99 _onnxruntime _cpu _test" \
     --scenario=Offline \
     --mode=performance \
     --test_query_count=10 \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --adr.compiler.tags=gcc \
     --quiet \
     --rerun

It will run for a few seconds and you should see an output similar to the following one at the end (the QPS is the performance result of this benchmark that depends on the speed of your system):

================================================
MLPerf Results Summary
================================================
SUT name : PySUT
Scenario : Offline
Mode     : PerformanceOnly
Samples per second: 3.71597
Result is : VALID
  Min duration satisfied : Yes
  Min queries satisfied : Yes
  Early stopping satisfied: Yes

================================================
Additional Stats
================================================
Min latency (ns)                : 267223684
Max latency (ns)                : 2691085775
Mean latency (ns)               : 1478150052
50.00 percentile latency (ns)   : 1612665856
90.00 percentile latency (ns)   : 2691085775
95.00 percentile latency (ns)   : 2691085775
97.00 percentile latency (ns)   : 2691085775
99.00 percentile latency (ns)   : 2691085775
99.90 percentile latency (ns)   : 2691085775

================================================
Test Parameters Used
================================================
samples_per_query : 10
target_qps : 1
target_latency (ns): 0
max_async_queries : 1
min_duration (ms): 0
max_duration (ms): 0
min_query_count : 1
max_query_count : 10
qsl_rng_seed : 148687905518835231
sample_index_rng_seed : 520418551913322573
schedule_rng_seed : 811580660758947900
accuracy_log_rng_seed : 0
accuracy_log_probability : 0
accuracy_log_sampling_target : 0
print_timestamps : 0
performance_issue_unique : 0
performance_issue_same : 0
performance_issue_same_index : 0
performance_sample_count : 10833

No warnings encountered during test.

No errors encountered during test.

Note that the MLPerf BERT inference throughput (samples per second) is very low because we use an unoptimized reference implementation of this benchmark on CPU.

Prepare minimal MLPerf submission to the SCC committee

You are now ready to generate the submission similar to the ones appearing on the official MLPerf inference dashboard.

Don't forget to record timestamps to the file mlperf_submission_short.tstamps before and after CM command.

You will use another CM script that runs the MLPerf inference benchmark in both accuracy and performance mode, runs the submission checker, unifies output for a dashboard and creates a valid MLPerf submission pack in mlperf_submission_short.tar.gz with all required MLPerf logs and stats.

You can run this script as follows:

cmr "run mlperf inference generate-run-cmds _submission _short" \
      --submitter="SCC23" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=onnxruntime \
      --device=cpu \
      --scenario=Offline \
      --execution-mode=test \
      --test_query_count=10 \
      --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
      --adr.mlperf-implementation.version=custom \
      --quiet \
      --output_tar=mlperf_submission_short.tar.gz \
      --output_summary=mlperf_submission_short_summary \
      --clean
  • --execution-mode=valid (Also remove _short tag) can be used to do a full valid run and in this mode --test_query_count is ignored and the loadgen generates the number of queries for a 10-minute run based on --target_qps value from a previous run. We can also override this value by giving --offline_target_qps=<> in case the estimated value from a test run turns out to be inaccurate.
  • We can also use custom (public/private) fork of the inference repository to enable custom changes to the harness code. For this you can change "ctuning" in the https://github.com/ctuning/inference,_branch.scc23 to your username and can even change the branch name.

It will take a few minutes to run and you should see the following output in the end:

[2023-09-26 19:20:42,245 submission_checker1.py:3308 INFO] Results open/SCC23/results/default-reference-cpu-onnxruntime-v1.15.1-default_config/bert-99/offline 3.72101
[2023-09-26 19:20:42,245 submission_checker1.py:3310 INFO] ---
[2023-09-26 19:20:42,245 submission_checker1.py:3395 INFO] ---
[2023-09-26 19:20:42,245 submission_checker1.py:3396 INFO] Results=1, NoResults=0, Power Results=0
[2023-09-26 19:20:42,245 submission_checker1.py:3403 INFO] ---
[2023-09-26 19:20:42,245 submission_checker1.py:3404 INFO] Closed Results=0, Closed Power Results=0

[2023-09-26 19:20:42,245 submission_checker1.py:3409 INFO] Open Results=1, Open Power Results=0

[2023-09-26 19:20:42,245 submission_checker1.py:3414 INFO] Network Results=0, Network Power Results=0

[2023-09-26 19:20:42,245 submission_checker1.py:3419 INFO] ---
[2023-09-26 19:20:42,245 submission_checker1.py:3421 INFO] Systems=1, Power Systems=0
[2023-09-26 19:20:42,245 submission_checker1.py:3422 INFO] Closed Systems=0, Closed Power Systems=0
[2023-09-26 19:20:42,245 submission_checker1.py:3427 INFO] Open Systems=1, Open Power Systems=0
[2023-09-26 19:20:42,245 submission_checker1.py:3432 INFO] Network Systems=0, Network Power Systems=0
[2023-09-26 19:20:42,245 submission_checker1.py:3437 INFO] ---
[2023-09-26 19:20:42,245 submission_checker1.py:3442 INFO] SUMMARY: submission looks OK
/usr/bin/python3 /home/arjun/CM/repos/local/cache/0cfdde8a3bd64fb6/inference/tools/submission/generate_final_report.py --input summary.csv
=========================================================
Searching for summary.csv ...
Converting to json ...

                                                                           0
Organization                                                           SCC23
Availability                                                       available
Division                                                                open
SystemType                                                              edge
SystemName                                                           default
Platform                   default-reference-cpu-onnxruntime-v1.15.1-defa...
Model                                                                bert-99
MlperfModel                                                          bert-99
Scenario                                                             Offline
Result                                                               3.72101
Accuracy                                                                70.0
number_of_nodes                                                            1
host_processor_model_name                AMD Ryzen 9 7950X 16-Core Processor
host_processors_per_node                                                   1
host_processor_core_count                                                 16
accelerator_model_name                                                   NaN
accelerators_per_node                                                      0
Location                       open/SCC23/results/default-reference-cpu-o...
framework                                                onnxruntime v1.15.1
operating_system             Ubuntu 22.04 (linux-6.2.0-32-generic-glibc2.35)
notes                      Powered by MLCommons Collective Mind framework...
compliance                                                                 1
errors                                                                     0
version                                                                 v3.1
inferred                                                                   0
has_power                                                              False
Units                                                              Samples/s

Note that --clean flag cleans all previous runs of MLPerf benchmark to make sure that the MLPerf submission script picks up the latest results.

Please submit the following files to the SCC'23 committee to get the first (minimum) set of points for managing to run the MLPerf BERT inference benchmark on your system:

  • mlperf_submission_short.tar.gz - automatically generated file with validated MLPerf results.
  • mlperf_submission_short_summary.json - automatically generated summary of MLPerf results.
  • mlperf_submission_short.run - CM commands to run MLPerf BERT inference benchmark saved to this file.
  • mlperf_submission_short.tstamps - execution timestamps before and after CM command saved to this file.
  • mlperf_submission_short.md - description of your platform and some highlights of the MLPerf benchmark execution.

You can find sample artifacts in this shared drive.

Note that by default, CM-MLPerf will store the raw results in $HOME/mlperf_submission (with truncated accuracy logs) and in $HOME/mlperf_submission_logs (with complete and very large accuracy logs).

You can change this directory using the flag --submission_dir={directory to store raw MLPerf results} in the above script.

You can also add --j flag to see the JSON output of this CM script (MLPerf inference workflow) that you can reuse in higher-level automations and GUI:

cmr "run mlperf inference generate-run-cmds _submission _short" \
      --submitter="SCC23" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=onnxruntime \
      --device=cpu \
      --scenario=Offline \
      --execution-mode=test \
      --test_query_count=10 \
      --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
      --adr.mlperf-implementation.version=custom \
      --quiet \
      --output_tar=mlperf_submission_short.tar.gz \
      --output_summary=mlperf_submission_short_summary \
      --clean \
      --j

or

import cmind
import json

r=cmind.access({'action':'run',
                'automation':'script',
                'out':'con',
                'tags':'run,mlperf,inference,generate-run-cmds,_submission,_short',
                'submitter':'SCC23',
                'hw_name':'default',
                'implementation':'reference',
                'model':'bert-99',
                'backend':'onnxruntime',
                'device':'cpu',
                'scenario':'Offline',
                'execution-mode':'test',
                'test_query_count':'10',
                'adr':{
                   'mlperf-implementation':{'tags':'_repo.https://github.com/ctuning/inference,_branch.scc23'},
                   'mlperf-implementation':{'version':'custom'}
                },
                'quiet':True,
                'output_tar':'mlperf_submission_short.tar.gz',
                'output_summary':'mlperf_submission_short_summary',
                'clean':True
                })

print (json.dumps(r, indent=2))

JSON output:

{
  "return": 0,
  "new_state": {
    "app_mlperf_inference_log_summary": {
      "sut name": "PySUT",
      "scenario": "Offline",
      "mode": "PerformanceOnly",
      "samples per second": "0.599291",
      "result is": "VALID",
      "min duration satisfied": "Yes",
      "min queries satisfied": "Yes",
      "early stopping satisfied": "Yes",
      "min latency (ns)": "2886616648",
      "max latency (ns)": "16686371378",
      "mean latency (ns)": "9949423118",
      "50.00 percentile latency (ns)": "10739129219",
      "90.00 percentile latency (ns)": "16686371378",
      "95.00 percentile latency (ns)": "16686371378",
      "97.00 percentile latency (ns)": "16686371378",
      "99.00 percentile latency (ns)": "16686371378",
      "99.90 percentile latency (ns)": "16686371378",
      "samples_per_query": "10",
      "target_qps": "1",
      "target_latency (ns)": "0",
      "max_async_queries": "1",
      "min_duration (ms)": "0",
      "max_duration (ms)": "0",
      "min_query_count": "1",
      "max_query_count": "10",
      "qsl_rng_seed": "148687905518835231",
      "sample_index_rng_seed": "520418551913322573",
      "schedule_rng_seed": "811580660758947900",
      "accuracy_log_rng_seed": "0",
      "accuracy_log_probability": "0",
      "accuracy_log_sampling_target": "0",
      "print_timestamps": "0",
      "performance_issue_unique": "0",
      "performance_issue_same": "0",
      "performance_issue_same_index": "0",
      "performance_sample_count": "10833"
    },
    "app_mlperf_inference_measurements": {
      "starting_weights_filename": "model.onnx",
      "retraining": "no",
      "input_data_types": "fp32",
      "weight_data_types": "fp32",
      "weight_transformations": "none"
    },
    "app_mlperf_inference_accuracy": {
      "exact_match": 70.0,
      "f1": 70.0
    }
  },
  "deps": [
    "detect,os",
    "detect,cpu",
    "get,python3",
    "get,mlcommons,inference,src",
    "get,sut,description",
    "generate,mlperf,inference,submission"
  ]
}

Optional: publish results at the live SCC'23 dashboard

You can publish your above results on a live SCC'23 dashboard by simply adding _dashboard variation with --dashboard_wb_project flag:

cmr "run mlperf inference generate-run-cmds _submission _short _dashboard" \
      --submitter="SCC23-{TEAM_NUMBER_AND_NAME}" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=onnxruntime \
      --device=cpu \
      --scenario=Offline \
      --execution-mode=test \
      --test_query_count=10 \
      --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
      --adr.mlperf-implementation.version=custom \
      --dashboard_wb_project=cm-mlperf-scc23-bert-offline \
      --quiet \
      --clean

Optional: debug reference implementation

Here we explain you how to debug MLPerf inference benchmark implementations when using CM.

You can add --debug flag to any of the above commands to run the MLPerf inference benchmark. CM will open a shell just before executing the benchmark implementation and with all preset environment variables. You can then copy/paste prepared command to run MLPerf natively or via gdb and then exit shell to finish the CM script.

You can also use GDB via environment variable --env.CM_RUN_PREFIX="gdb --args " instead of opening a shell.

Optional: extend reference implementation

You can locate the sources of the MLPerf inference reference implementation in the CM cache using the following command:

cm show cache "clone inference _branch.scc23"

You can then locate and change/updated/extend the file inference/language/bert/onnxruntime_SUT.py in the above CM cache path with the MLPerf BERT inference benchmark and ONNX backend and rerun CM commands to run MLPerf benchmark with your changes. You can explore other backends in that path too.

Optional: use another compatible BERT model (for example from the Hugging Face Hub)

You can manually download some compatible BERT models and use them with the MLPerf inference benchmark via CM as follows.

First download a model to your local host such as $HOME directory. Here is an example of the original BERT MLPerf FP32 model from the Hugging Face Hub:

wget https://huggingface.co/ctuning/mlperf-inference-bert-onnx-fp32-squad-v1.1/resolve/main/model.onnx

Then add the following flags to above commands to run MLPerf inference:

 --env.CM_MLPERF_CUSTOM_MODEL_PATH=$HOME/model.onnx \
 --env.CM_ML_MODEL_FULL_NAME=bert-99-custom \

For example, you can measure performance of this model in a short MLPerf run as follows:

cmr "app mlperf inference generic _python _bert-99 _onnxruntime _cpu _test" \
     --scenario=Offline \
     --mode=performance \
     --test_query_count=10 \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --env.CM_MLPERF_CUSTOM_MODEL_PATH=$HOME/model.onnx \
     --env.CM_ML_MODEL_FULL_NAME=bert-99-custom \
     --adr.compiler.tags=gcc \
     --quiet \
     --rerun

Optional (research): pruning and benchmarking BERT models

You can check our reproducibility initiative for ACM/IEEE/NeurIPS conferences, prune BERT model based on NeurIPS 2022 paper "A Fast Post-Training Pruning Framework for Transformers" using this CM script and feed a newly pruned BERT model to the MLPerf inference benchmarking using --env.CM_MLPERF_CUSTOM_MODEL_PATH flag in the above command.

We also suggest you to check another related project from Hugging Face, add CM interface and test it with the MLPerf inference benchmark.

Please contact us via the public Discord server to participate in this collaborative R&D.

Optional: use another ML framework

You can run MLPerf inference benchmark with other ML frameworks using the CM variation _pytorch or _tf instead of onnxruntime:

PyTorch

cmr "app mlperf inference generic _python _bert-99 _pytorch _cpu _test" \
     --scenario=Offline \
     --mode=performance \
     --test_query_count=10 \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --adr.compiler.tags=gcc \
     --quiet \
     --rerun

Note that CM will attempt to automatically download BERT large model in PyTorch format from Zenodo (~1.3GB) and plug it into the CM-MLPerf pipeline.

TensorFlow

cmr "app mlperf inference generic _python _bert-99 _tf _cpu _test" \
     --scenario=Offline \
     --mode=performance \
     --test_query_count=10 \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --adr.compiler.tags=gcc \
     --quiet \
     --rerun

Note that CM will attempt to automatically download BERT large model in PyTorch format from Zenodo (~1.3GB) and plug it into the CM-MLPerf pipeline.

Optional: use CUDA with reference implementation

You can install or detect CUDA drivers, toolkit and cuDNN via CM as follows:

cmr "install prebuilt-cuda _driver"
cmr "get cuda _cudnn"

You can print info about CUDA devices via CM as follows:

cmr "get cuda-devices"

You can now run MLPerf inference benchmark with PyTorch and CUDA as follows:

cmr "app mlperf inference generic _python _bert-99 _pytorch _cuda _test" \
     --scenario=Offline \
     --mode=performance \
     --test_query_count=10 \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --adr.compiler.tags=gcc \
     --quiet \
     --rerun

Run optimized implementation of the MLPerf inference BERT benchmark

Now you are ready to run optimized implementations of the MLPerf inference benchmark for the hardware that you want to showcase at SCC'23.

You will get extra points proportional to the MLPerf BERT inference throughput obtained on your system. You will also get the major bonus points for any improvements to the MLPerf inference implementation including support for new hardware such as AMD GPUs.

Note that the accuracy of the BERT model (F1 score) should be always within 99% of 90.874.

Showcase CPU performance (x64 or Arm64)

If you plan to showcase the CPU performance of your system, we suggest you to run the NeuralMagic implementation of the MLPerf BERT inference benchmark that obtains competitive performance on x86 and Arm64 CPUs. Note that it can take around 25..30 min to complete.

The MLPerf implementation for DeepSparse backend is available in this repo and will be automatically installed by CM.

Don't forget to set this environment if you use Python virtual environment installed via CM:

export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"

Run quantized and pruned BERT model (int8) on CPU

You can now run of the MLPerf inference benchmark with quantized and pruned Int8 BERT model, batch size of 128 and DeepSparse backend via CM while measuring accuracy, performance and preparing your submission similar to the official one as follows:

cmr "run mlperf inference generate-run-cmds _submission _short" \
      --submitter="SCC23" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=deepsparse \
      --device=cpu \
      --scenario=Offline \
      --execution-mode=test \
      --test_query_count=10000 \
      --adr.mlperf-inference-implementation.max_batchsize=128 \
      --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni \
      --dashboard_wb_project=cm-mlperf-scc23-bert-offline \
      --quiet \
      --output_tar=mlperf_submission_1.tar.gz \
      --output_summary=mlperf_submission_1_summary \
      --clean

Prepare optimized MLPerf submission to the SCC committee

You will need to submit the following files with the optimized MLPerf BERT inference results to obtain main points (including major bonus points for improving existing benchmark implementations and adding new hardware backends):

  • mlperf_submission_{N}.tar.gz - automatically generated file with validated MLPerf results.
  • mlperf_submission_{N}_summary.json - automatically generated summary of MLPerf results.
  • mlperf_submission_{N}.run - CM commands to run MLPerf BERT inference benchmark saved to this file.
  • mlperf_submission_{N}.tstamps - execution timestamps before and after CM command saved to this file.
  • mlperf_submission_{N}.md - your highlights, optimizations, improvements and extensions of the MLPerf BERT inference benchmark (new hardware backends, support for multi-node execution, batch size, quantization, etc). Note that you will need to provide a PR with open-source Apache 2.0 improvements to the MLCommons inference repo our our stable fork.

where N is your attempt number out of 5.

Optional: optimize/tune batch size using CM experiment automation

You can now tune batch size of the MLPerf inference benchmark using the CM experiment automation:

cm run experiment --tags=tuning,mlperf,bert,deepsparse,cpu,batch-size -- \
     cmr "run mlperf inference generate-run-cmds _submission _short" \
           --submitter="SCC23" \
           --hw_name=default \
           --implementation=reference \
           --model=bert-99 \
           --backend=deepsparse \
           --device=cpu \
           --scenario=Offline \
           --execution-mode=test \
           --test_query_count=10000 \
           --adr.mlperf-inference-implementation.max_batchsize="{{BATCH_SIZE{[8,16,32,64,128,256,192,384]}}}" \
           --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni \
           --dashboard_wb_project=cm-mlperf-scc23-bert-offline \
           --quiet \
           --output_tar=mlperf_submission_1.tar.gz \
           --output_summary=mlperf_submission_1_summary \
           --submission_dir="{{CM_EXPERIMENT_PATH3}}/output" \
           --clean

CM experiment will create a new CM experiment artifact in the local repository with tags tuning,mlperf,bert,deepsparse,cpu,batch-size and will record all MLPerf artifacts together with input/output in separate directories there.

It allows you to encapsulate, analyze and replay multiple experiments.

CM experiment will detect {{BATCH_SIZE{[8,16,32,64,128,256,192,384]}}} after -- and will substitute it with Python function eval("[8,16,32,64,128,256,192,384"]). If it is a list, CM experiment will iterate over it and run the command after --.

{{CM_EXPERIMENT_PATH3}} will be substituted with the CM holder directory for experiment artifacts.

You can find this CM entry with all MLPerf artifacts as follows:

cm find experiment --tags=tuning,mlperf,bert,deepsparse,cpu,batch-size

You can replay some experiments as follows:

cm replay experiment --tags=tuning,mlperf,bert,deepsparse,cpu,batch-size

Optional: debug DeepSparse implementation

Just add --debug flag to the above commands. CM will open a shell just before executing the MLPerf BERT inference benchmark with DeepSparse backend and with all preset environment variables. You can then copy/paste prepared command to run MLPerf natively or via gdb and then exit shell to finish the CM script.

You can also use GDB via environment variable --env.CM_RUN_PREFIX="gdb --args " instead of opening a shell.

Optional: extend this implementation

If you want to extend this implementation, you can locate the sources of the MLPerf inference implementation with DeepSparse backend in the CM cache using the following command:

cm show cache "clone inference _branch.deepsparse"

You can then locate and change/updated/extend the file inference/language/bert/deepsparse_SUT.py in the above CM cache path with the MLPerf BERT inference benchmark and rerun CM commands to run MLPerf benchmark with your changes.

Optional: use another compatible BERT model with DeepSparse backend

You can manually download some compatible BERT models and use them with the MLPerf inference benchmark via CM as follows.

First download a model to your local host such as $HOME directory. Here is an example of the BERT-large int8 model without sparsification from the NeuralMagic Zoo (~330MB):

wget https://api.neuralmagic.com/v2/models/fac19d9e-b489-450b-b077-f1d6a2a68735/files/deployment/model.onnx?version=2 -O model2.onnx

Then add the following flags to above commands to run MLPerf inference:

 --env.CM_MLPERF_CUSTOM_MODEL_PATH=$HOME/model2.onnx \
 --env.CM_ML_MODEL_FULL_NAME=bert-99-custom \

For example, you can prepare submission as follows:

cmr "run mlperf inference generate-run-cmds _submission _short _dashboard" \
      --submitter="SCC23" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=deepsparse \
      --device=cpu \
      --scenario=Offline \
      --execution-mode=test \
      --test_query_count=2000 \
      --adr.mlperf-inference-implementation.max_batchsize=128 \
      --env.CM_MLPERF_CUSTOM_MODEL_PATH=$HOME/model2.onnx \
      --env.CM_ML_MODEL_FULL_NAME=bert-99-custom \
      --dashboard_wb_project=cm-mlperf-scc23-bert-offline \
      --quiet \
      --output_tar=mlperf_submission_2.tar.gz \
      --output_summary=mlperf_submission_2_summary \
      --clean

Please check a related script from the cTuning foundation used to prepare MLPerf inference v3.1 submissions with multiple BERT model variations:

Optional: use another compatible BERT model from the NeuralMagic Zoo directly (fp32)

You can find and use any compatible model from the NeuralMagic Zoo with the MLPerf inference benchmark via CM as follows:

cmr "run mlperf inference generate-run-cmds _submission _short" \
      --submitter="SCC23" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=deepsparse \
      --device=cpu \
      --scenario=Offline \
      --execution-mode=valid \
      --adr.mlperf-inference-implementation.max_batchsize=128 \
      --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50-none-vnni \
      --quiet \
      --output_tar=mlperf_submission_3.tar.gz \
      --output_summary=mlperf_submission_3_summary \
      --clean

Please check related scripts from the cTuning foundation used to prepare MLPerf inference v3.1 submissions with multiple BERT model variations from the NeuralMagic Zoo:

Showcase Nvidia GPU performance

Please follow this README to run the MLPerf BERT inference benchmark on Nvidia GPU(s).

A summary of CM commands you may need to run Nvidia's implementation of the MLPerf inference benchmark while adapting to your environment unless you want to use a container (note that Nvidia setup will be interactive and you will need answer a few questions about your system):

cmr "install prebuilt-cuda _driver"

cmr "get cudnn" --tar_file={full path to the cuDNN tar file downloaded from https://developer.nvidia.com/cudnn}

cmr "get tensorrt _dev" --tar_file={full path to the TensorRT tar file downloaded from https://developer.nvidia.com/tensorrt-download}

cmr "generate-run-cmds inference _find-performance" \
    --submitter="SCC23" \
    --hw_name=default \
    --implementation=nvidia-original \
    --model=bert-99 \
    --backend=tensorrt \
    --device=cuda \
    --scenario=Offline \
    --category=edge \
    --division=open \
    --quiet \
    --clean

cmr "generate-run-cmds inference _submission" \
    --submitter="SCC23" \
    --hw_name=default \
    --implementation=nvidia-original \
    --model=bert-99 \
    --backend=tensorrt \
    --device=cuda \
    --scenario=Offline \
    --category=edge \
    --division=open \
    --execution-mode=valid \
    --quiet \
    --output_tar=mlperf_submission_1.tar.gz \
    --output_summary=mlperf_submission_1_summary \
    --clean

Note, that since CM attempts to adapt MLPerf to your environment, your combination of dependencies may not have been tested by the community and may sometimes fail. In such case, please report issues here to help the community continuously and collaboratively improve CM workflows and make them more portable (that's why we called our automation language "Collective Mind").

For example, you may often need to upgrade protobuf to the latest version until the community adds a better handling of the protobuf version to the CM-MLPerf pipeline:

pip install --upgrade protobuf

Prepare optimized MLPerf submission to the SCC committee

You will need to submit the following files with the optimized MLPerf BERT inference results to obtain more points proportional to your performance (in comparison with other teams using Nvidia GPUs):

  • mlperf_submission_{N}.tar.gz - automatically generated file with validated MLPerf results.
  • mlperf_submission_{N}_summary.json - automatically generated summary of MLPerf results.
  • mlperf_submission_{N}.run - CM commands to run MLPerf BERT inference benchmark saved to this file.
  • mlperf_submission_{N}.tstamps - execution timestamps before and after CM command saved to this file.
  • mlperf_submission_{N}.md - description of your submission

where N is your attempt number out of 5.

You will need to get in touch with Nvidia if you want to optimize this submission further.

For example, our Nvidia colleagues shared the following suggestions that may improve performance of their implementations wrapped and unified by CM:

Showcase AMD performance

There is a pilot CM support to run MLPerf BERT inference on AMD GPU with ROCm.

You can test it as follows:

cmr "app mlperf inference generic _python _bert-99 _onnxruntime _rocm _test" \
     --scenario=Offline \
     --mode=performance \
     --test_query_count=10 \
     --rerun \
     --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
     --adr.mlperf-implementation.version=custom \
     --adr.compiler.tags=gcc \
     --quiet

You can then prepare a submission as follows:

cmr "run mlperf inference generate-run-cmds _submission _short" \
      --submitter="SCC23" \
      --hw_name=default \
      --implementation=reference \
      --model=bert-99 \
      --backend=onnxruntime \
      --device=rocm \
      --scenario=Offline \
      --execution-mode=test \
      --test_query_count=1000 \
      --adr.mlperf-implementation.tags=_repo.https://github.com/ctuning/inference,_branch.scc23 \
      --adr.mlperf-implementation.version=custom \
      --quiet \
      --output_tar=mlperf_submission_2.tar.gz \
      --output_summary=mlperf_submission_2_summary \
      --clean

You will see a long output that should contain the following line with accuracy (to make sure that MLPerf works properly):

{"exact_match": 70.0, "f1": 70.0}

Please get in touch with the community via Discord server if you encounter issues or would like to extend it!

Prepare optimized MLPerf submission to the SCC committee

You will need to submit the following files with the optimized MLPerf BERT inference results to obtain main points (including major bonus points for improving existing benchmark implementations and adding new hardware backends):

  • mlperf_submission_{N}.tar.gz - automatically generated file with validated MLPerf results.
  • mlperf_submission_{N}_summary.json - automatically generated summary of MLPerf results.
  • mlperf_submission_{N}.run - CM commands to run MLPerf BERT inference benchmark saved to this file.
  • mlperf_submission_{N}.tstamps - execution timestamps before and after CM command saved to this file.
  • mlperf_submission_{N}.md - your highlights, optimizations, improvements and extensions of the MLPerf BERT inference benchmark (new hardware backends, support for multi-node execution, batch size, quantization, etc). Note that you will need to provide a PR with open-source Apache 2.0 improvements to the MLCommons inference repo our our stable fork.

where N is your attempt number out of 5.

The next steps

The MLCommons Task Force on Automation and Reproducibility and the cTuning foundation continue working with the community to enable universal benchmarking of AI/ML systems across any model, data set, software and hardware using CM and loadgen. We are also developing a universal Python and C++ harness to make it easier to plug in different models, data sets, frameworks and hardware backends together with a user-friendly GUI/platform to run, compare and reproduce ML(Perf) benchmarks. We welcome other MLPerf and CM extensions including support for multi-node execution, better implementations, optimizations and new hardware backends.

Please join our Discord server to provide your feedback and participate in these community developments!

Acknowledgments

This tutorial, the MLCommons CM automation language, CM scripts and CM automation workflows for MLPerf were developed by Grigori Fursin and Arjun Suresh (cTuning foundation and cKnowledge.org) in collaboration with the community and MLCommons.

We thank Miro Hodak, Mitchelle Rasquinha, Amiya K. Maji, Ryan T DeRue, Michael Goin, Kasper Mecklenburg, Lior Khermosh, James Goel, Jinho Suh, Thomas Zhu, Peter Mattson, David Kanter, Vijay Janappa Reddi and the community for their feedback, suggestions and contributions!

Nvidia MLPerf inference backend

Nvidia's MLPerf inference implementation was developed by Zhihan Jiang, Ethan Cheng, Yiheng Zhang and Jinho Suh.

DeepSparse MLPerf inference backend

We thank Michael Goin from Neural Magic for fruitful collaboration to add DeepSparse backend for x86-64 and Arm64 CPU targets to MLPerf inference benchmarks and submit many competitive BERT results across diverse hardware to MLPerf inference v3.1.