Byte MLPerf Inference Benchmark Tool

Byte MLPerf(Inference) is an AI Accelerator Benchmark that focuses on evaluating AI Accelerators from practical production perspective, including the ease of use and versatility of software and hardware. Byte MLPerf has the following characteristics:

Models and runtime environments are more closely aligned with practical business use cases.
For ASIC hardware evaluation, besides evaluate performance and accuracy, it also measure metrics like compiler usability and coverage.
Performance and accuracy results obtained from testing on the open Model Zoo serve as reference metrics for evaluating ASIC hardware integration.

Vendors can refer to this document for guidance on building backend: ByteMLPerf Guide [中文版] Also, you can visit our offical website here:bytemlperf.ai

Usage

The user uses launch.py as the entry point. When using Byte MLPerf to evaluate the model, you only need to pass in two parameters --task and --hardware_type, as shown below:

python3 launch.py --task xxx --hardware_type xxx

task --task parameter is the name of the incoming workload. You need to specify the workload. For example, if you would like to evaluate the workload: bert-tf-fp16.json, you need to specify --task bert-tf-fp16. Note: All workloads are defined under byte_mlperf/workloads, and the name needs to be aligned with the file name when passing parameters. The current format is model-framework-precision.
hardware_type --hardware_type parameter is the incoming hardware_type name, there is no default value, it must be specified by the user. Example: To evaluate Habana Goya, specify --hardware_type GOYA . Note: All hardware types are defined under byte_mlperf/backends, and the name needs to be aligned with the folder name when passing parameters.
compile_only --compile_only parameter will make task stoped once compilation is finished
show_task_list --show_task_list parameter will print all task name
show_hardware_list --show_hardware_list parameter will print all hardware backend

Workload Description

A workload definition needs to contain the following fields:

{
    "model": "bert-torch-fp32",   //The name of the model to be evaluated, which needs to be aligned with the model_zoo name
    "test_perf": true,            //Evaluate model performance
    "test_accuracy": true,        //Evaluate model accuracy
    "test_numeric": true,         //Accuracy：Evaluate model numeric
    "clients": 3,                 //Performance：Client threads that submit data
    "iterations": 100,            //Performance：How many iterations are submitted by each thread
    "batch_sizes":[1,4,8,16,32,64],//Performance：The batch size when each thread submits data
    "data_percent": 50,           //Accuracy：Ratio of data to assess accuracy, [1-100]
    "compile_only": false,           //Compile the model only
}

Model Zoo List

Model Zoo&Dataset The models supported by Byte MLPerf are collected under the Model Zoo. From the perspective of access rights, they are currently divided into internal models and open models. Released with Byte MLPerf is the open model included in the corresponding version.

Open model collection principles:

Basic Model: including Resnet50, Bert and WnD;
Popular Model：Includes models currently widely used in the industry;
SOTA: including SOTA models corresponding to business domains;

In addition to the complete model structure, Byte MLPerf will also add some typical model substructure subgraphs or OPs (provided that the open model cannot find a suitable model containing such classic substructures), such as transformer encoder/decoder with different sequence lengths , all kinds of common conv ops, such as group conv, depwise-conv, point-wise conv, and rnn common structures, such as gru/lstm, etc.

Model	Domain	Purpose	Framework	Dataset	Precision
resnet50-v1.5	cv	regular	tensorflow, pytorch	imagenet2012	fp32
bert-base	nlp	regular	tensorflow, pytorch	squad-1.1	fp32
wide&deep	rec	regular	tensorflow	criteo	fp32
videobert	mm	popular	onnx	cifar100	fp32
albert	nlp	popular	pytorch	squad-1.1	fp32
conformer	nlp	popular	onnx	none	fp32
roformer	nlp	popular	tensorflow	cail2019	fp32
yolov5	cv	popular	onnx	none	fp32
roberta	nlp	popular	pytorch	squad-1.1	fp32
deberta	nlp	popular	pytorch	squad-1.1	fp32
swin-transformer	cv	popular	pytorch	imagenet2012	fp32
gpt2	nlp	sota	pytorch	none	fp32
stable diffusion	cv	sota	onnx	none	fp32
LlaMa2 7B	nlp	sota	torch	none	fp16
chatGLM2 6B	nlp	sota	torch	none	fp16

ByteIR

The ByteIR Project is a ByteDance model compilation solution. ByteIR includes compiler, runtime, and frontends, and provides an end-to-end model compilation solution.

Although all ByteIR components (compiler/runtime/frontends) are together to provide an end-to-end solution, and all under the same umbrella of this repository, each component technically can perform independently.

For More Information, please refer to ByteIR

Models Supported By ByteIR:

Model	Domain	Purpose	Framework	Dataset	Precision
resnet50-v1.5	cv	regular	mhlo	imagenet2012	fp32
bert-base	nlp	regular	mhlo	squad-1.1	fp32

Vendor List

ByteMLPerf Vendor Backend List will be shown below

Vendor	SKU	Key Parameters	Supplement
Intel	Xeon	-	-
Stream Computing	STC P920	Computation Power:128 TFLOPS@FP16 Last Level Buffer: 8MB, 256GB/s Level 1 Buffer: 1.25MB, 512GB/s Memory: 16GB, 119.4GB/S Host Interface：PCIe 4, 16x, 32GB/s TDP: 160W	STC Introduction
Graphcore	Graphcore® C600	Compute: 280 TFLOPS@FP16, 560 TFLOPS@FP8 In Processor Memory: 900 MB, 52 TB/s Host Interface: Dual PCIe Gen4 8-lane interfaces, 32GB/s TDP: 185W	IPU Introduction
Moffett-AI	Moffett-AI S30	Compute: 1440 (32x-Sparse) TFLOPS@BF16, 2880 (32x-Sparse) TOPS@INT8, Memory: 60 GB, Host Interface: Dual PCIe Gen4 8-lane interfaces, 32GB/s TDP: 250W	SPU Introduction
Habana	Gaudi2	24 Tensor Processor Cores, Dual matrix multiplication engines Memory: 96 GB HBM2E, 48MB SRAM	HPU Introduction

Benchmark Summary

Benchmark Result Summary : QPS Perspective

Statement

ASF Statement on Compliance with US Export Regulations and Entity List

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