llama-recipes/recipes/benchmarks/fmbench

Benchmark Llama models on AWS

The FMBench tool provides a quick and easy way to benchmark the Llama family of models for price and performance on any AWS service including Amazon SagMaker, Amazon Bedrock or Amazon EKS or Amazon EC2 as Bring your own endpoint.

The need for benchmarking

Customers often wonder what is the best AWS service to run Llama models for my specific use-case and my specific price performance requirements. While model evaluation metrics are available on several leaderboards (HELM, LMSys), but the price performance comparison can be notoriously hard to find and even more harder to trust. In such a scenario, we think it is best to be able to run performance benchmarking yourself on either on your own dataset or on a similar (task wise, prompt size wise) open-source dataset (LongBench, QMSum). This is the problem that FMBench solves.

FMBench: an open-source Python package for FM benchmarking on AWS

FMBench runs inference requests against endpoints that are either deployed through FMBench itself (as in the case of SageMaker) or are available either as a fully-managed endpoint (as in the case of Bedrock) or as bring your own endpoint. The metrics such as inference latency, transactions per-minute, error rates and cost per transactions are captured and presented in the form of a Markdown report containing explanatory text, tables and figures. The figures and tables in the report provide insights into what might be the best serving stack (instance type, inference container and configuration parameters) for a given Llama model for a given use-case.

The following figure gives an example of the price performance numbers that include inference latency, transactions per-minute and concurrency level for running the Llama2-13b model on different instance types available on SageMaker using prompts for Q&A task created from the LongBench dataset, these prompts are between 3000 to 3840 tokens in length. Note that the numbers are hidden in this figure but you would be able to see them when you run FMBench yourself.

The following table (also included in the report) provides information about the best available instance type for that experiment¹.

Information	Value
experiment_name	llama2-13b-inf2.24xlarge
payload_file	payload_en_3000-3840.jsonl
instance_type	ml.inf2.24xlarge
concurrency	**
error_rate	**
prompt_token_count_mean	3394
prompt_token_throughput	2400
completion_token_count_mean	31
completion_token_throughput	15
latency_mean	**
latency_p50	**
latency_p95	**
latency_p99	**
transactions_per_minute	**
price_per_txn	**

¹ ** represent values hidden on purpose, these are available when you run the tool yourself.

The report also includes latency Vs prompt size charts for different concurrency levels. As expected, inference latency increases as prompt size increases but what is interesting to note is that the increase is much more at higher concurrency levels (and this behavior varies with instance types).

How to get started with FMBench

The following steps provide a Quick start guide for FMBench. For a more detailed DIY version, please see the FMBench Readme.

1. Launch the AWS CloudFormation template included in this repository using one of the buttons from the table below. The CloudFormation template creates the following resources within your AWS account: Amazon S3 buckets, Amazon IAM role and an Amazon SageMaker Notebook with this repository cloned. A read S3 bucket is created which contains all the files (configuration files, datasets) required to run FMBench and a write S3 bucket is created which will hold the metrics and reports generated by FMBench. The CloudFormation stack takes about 5-minutes to create.

|AWS Region | Link | |:------------------------:|:-----------:| |us-east-1 (N. Virginia) | | |us-west-2 (Oregon) | |

1. Once the CloudFormation stack is created, navigate to SageMaker Notebooks and open the fmbench-notebook.

2. On the fmbench-notebook open a Terminal and run the following commands.

   conda create --name fmbench_python311 -y python=3.11 ipykernel
   source activate fmbench_python311;
   pip install -U fmbench
   

3. Now you are ready to fmbench with the following command line. We will use a sample config file placed in the S3 bucket by the CloudFormation stack for a quick first run.

   1. We benchmark performance for the Llama2-7b model on a ml.g5.xlarge and a ml.g5.2xlarge instance type, using the huggingface-pytorch-tgi-inference inference container. This test would take about 30 minutes to complete and cost about $0.20.

   2. It uses a simple relationship of 750 words equals 1000 tokens, to get a more accurate representation of token counts use the Llama2 tokenizer (instructions are provided in the next section). It is strongly recommended that for more accurate results on token throughput you use a tokenizer specific to the model you are testing rather than the default tokenizer. See instructions provided later in this document on how to use a custom tokenizer.

      account=`aws sts get-caller-identity | jq .Account | tr -d '"'`
      region=`aws configure get region`
      fmbench --config-file s3://sagemaker-fmbench-read-${region}-${account}/configs/config-llama2-7b-g5-quick.yml >> fmbench.log 2>&1
      

   3. Open another terminal window and do a tail -f on the fmbench.log file to see all the traces being generated at runtime.

      tail -f fmbench.log
      

4. The generated reports and metrics are available in the sagemaker-fmbench-write-<replace_w_your_aws_region>-<replace_w_your_aws_account_id> bucket. The metrics and report files are also downloaded locally and in the results directory (created by FMBench) and the benchmarking report is available as a markdown file called report.md in the results directory. You can view the rendered Markdown report in the SageMaker notebook itself or download the metrics and report files to your machine for offline analysis.

The config.yml file

Each FMBench run works with a configuration file that contains the information about the model, the deployment steps, and the tests to run. A typical FMBench workflow involves either directly using an already provided config file from the configs folder in the FMBench GitHub repo or editing an already provided config file as per your own requirements (say you want to try benchmarking on a different instance type, or a different inference container etc.).

A simple config file with key parameters annotated is includes in this repo, see config.yml. This file benchmarks performance of Llama2-7b on an ml.g5.xlarge instance and an ml.g5.2xlarge instance.

🚨 Benchmarking Llama3 on Amazon SageMaker 🚨

Llama3 is now available on SageMaker (read blog post), and you can now benchmark it using FMBench. Here are the config files for benchmarking Llama3-8b-instruct and Llama3-70b-instruct on ml.p4d.24xlarge and ml.g5.12xlarge instance.

• Config file for Llama3-8b-instruct on ml.p4d.24xlarge and ml.g5.12xlarge
• Config file for Llama3-70b-instruct on ml.p4d.24xlarge and ml.g5.48xlarge

Benchmarking Llama2 on Amazon SageMaker

Llama2 models are available through SageMaker JumpStart as well as directly deployable from Hugging Face to a SageMaker endpoint. You can use FMBench to benchmark Llama2 on SageMaker for different combinations of instance types and inference containers.

• Config file for Llama2-7b on ml.g5.xlarge and ml.g5.2xlarge instances, using the Hugging Face TGI container.
• Config file for Llama2-7b on ml.g4dn.12xlarge instance using the Deep Java Library DeepSpeed container.
• Config file for Llama2-13b on ml.g5.12xlarge, ml.inf2.24xlarge and ml.p4d.24xlarge instances using the Hugging Face TGI container and the Deep Java Library & NeuronX container.
• Config file for Llama2-70b on ml.p4d.24xlarge instance using the Deep Java Library TensorRT container.
• Config file for Llama2-70b on ml.inf2.48xlarge instance using the HuggingFace TGI with Optimum NeuronX container.

Benchmarking Llama2 on Amazon Bedrock

The Llama2-13b-chat and Llama2-70b-chat models are available on Bedrock. You can use FMBench to benchmark Llama2 on Bedrock for both on-demand throughput and provisioned throughput inference options.

• Config file for Llama2-13b-chat and Llama2-70b-chat on Bedrock for on-demand throughput.

• For testing provisioned throughput simply replace the ep_name parameter in experiments section of the config file with the ARN of your provisioned throughput.

More..

For bug reports, enhancement requests and any questions please create a GitHub issue on the FMBench repo.