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FMBench Orchestrator

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Overview

The FMBench Orchestrator is a tool designed to automate the deployment and management of FMBench for benchmarking on Amazon EC2, Amazon SageMaker and Amazon Bedrock. In case of benchmarking on EC2, we could benchmark on multiple instances simultaneously, and these instances can be of different instance types (so you could run g6e, p4de and a trn1 instances via the same config file), in different AWS regions and also test multiple FMBench config files. This orchestrator automates the creation of Security Groups, Key Pairs, EC2 instances, runs FMBench for a specific config, retrieves the results, and shuts down the instances after completion. Thus it simplifies the benchmarking process (no more manual creation of SageMaker Notebooks, EC2 instances and cleanup, downloading results folder) and ensures a streamlined and scalable workflow.

+---------------------------+
| Initialization            |
| (Configure & Setup)       |
+---------------------------+
          ↓
+---------------------------+
| Instance Creation         |
| (Launch EC2 Instances)    |
+---------------------------+
          ↓
+---------------------------+
| FMBENCH Execution         |
| (Run Benchmark Script)    |
+---------------------------+
          ↓
+---------------------------+
| Results Collection        |
| (Download from instances) |
+---------------------------+
          ↓
+---------------------------+
| Instance Termination      |
| (Terminate Instances)     |
+---------------------------+

Prerequisites

Installation

  1. Install conda

    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b  # Run the Miniconda installer in batch mode (no manual intervention)
rm -f Miniconda3-latest-Linux-x86_64.sh    # Remove the installer script after installation
eval "$(/home/$USER/miniconda3/bin/conda shell.bash hook)" # Initialize conda for bash shell
conda init  # Initialize conda, adding it to the shell

  2. Clone the Repository

    git clone https://github.com/awslabs/fmbench-orchestrator.git
cd fmbench-orchestrator

Conda Environment Setup

  1. Create a Conda Environment with Python 3.11:

    conda create --name fmbench-orchestrator-py311 python=3.11 -y

  2. Activate the Environment:

    conda activate fmbench-orchestrator-py311

  3. Install Required Packages:

    pip install -r requirements.txt

  4. Hugging Face token:

    Most models and tokenizers are downloaded from Hugging Face, to enable this place your Hugging Face token in /tmp/hf_token.txt.

    # replace with your Hugging Face token
hf_token=your-hugging-face-token
echo $hf_token > /tmp/hf_token.txt

Steps to run the orchestrator:

You can either use an existing config file included in this repo, such as configs/ec2.yml or create your own using the files provided in the configs directory as a template. Make sure you are in the fmbench-orchestrator-py311 conda environment. The following command runs benchmarking for the Llama3-8b model on an g6e.2xlarge and g6e.4xlarge instance types.

The following command runs benchmarking on an EC2 instance, see Benchmark for SageMaker for benchmarking on SageMaker and Benchmark for Bedrock for benchmarking on Bedrock.

python main.py --config-file configs/ec2.yml

Here is a description of all the command line parameters that are supported by the orchestrator:

Once the run is completed you can see the FMBench results folder downloaded in the results directory under the orchestrator, the fmbench.log file is also downloaded from the EC2 instances and placed alongside the results folder.

To analyze the results i.e. compare and contrast the price performance of different EC2 instance types that were a part of the run by running an analytics script. The example below shows how to use the analytcs.py script to analyze results obtained from running the orchestrator with the llama3-8b-g6e-triton.yml config file.

python analytics/analytics.py --results-dir results/llama3-8b-g6e-triton --model-id llama3-8b --payload-file payload_en_3000-3840.jsonl --latency-threshold 2

Running the scripts above creates a results folder under analytics which contains summaries of the results and a heatmap that helps understand which instance type gives the best price performance at the desired scale (transactions/minute) while maintaining the inference latency below a desired threshold.

How do I ...

See configuration guide for details on the orchestrator config file.

Benchmark for EC2

Take an existing config file from the configs folder, create a copy and edit it as needed. You would typically only need to modify the instances section of the config file to either modify the instance type and config file or add additional types. For example the following command line benchmarks the Llama3.1-8b models on g6e EC2 instance types.

python main.py --config-file configs/ec2.yml

Benchmark for SageMaker

You can benchmark any model(s) on Amazon SageMaker by simply pointing the orchestrator to the desired FMBench SageMaker config file. The orchestrator will create an EC2 instance and use that for running FMBench benchmarking for SageMaker. For example the following command line benchmarks the Llama3.1-8b models on ml.g5 instance types on SageMaker.

# provide the name of an S3 bucket in which you want
# SageMaker to store the model files (for models downloaded
# from Hugging Face)
write_bucket=your-bucket-name
python main.py --config-file configs/sagemaker.yml --fmbench-config-file fmbench:llama3.1/8b/config-llama3.1-8b-g5.xl-g5.2xl-sm.yml --write-bucket $write_bucket

Benchmark for Bedrock

You can benchmark any model(s) on Amazon Bedrock by simply pointing the orchestrator to the desired FMBench SageMaker config file. The orchestrator will create an EC2 instance and use that for running FMBench benchmarking for Bedrock. For example the following command line benchmarks the Llama3.1 models on Bedrock.

python main.py --config-file configs/bedrock.yml --fmbench-config-file fmbench:bedrock/config-bedrock-llama3-1.yml

Use an existing FMBench config file but modify it slightly for my requirements

  1. Download an FMBench config file from the FMBench repo and place it in the configs/fmbench folder.

  2. Modify the downloaded config as needed.

  3. Update the instance -> fmench_config section for the instance that needs to use this file to point to the updated config file in fmbench/configs so for example if the updated config file was config-ec2-llama3-8b-g6e-2xlarge-custom.yml then the following parameter:

    fmbench_config: 
- fmbench:llama3/8b/config-ec2-llama3-8b-g6e-2xlarge.yml

    would be changed to:

    fmbench_config: 
- configs/fmbench/config-ec2-llama3-8b-g6e-2xlarge-custom.yml

    The orchestrator would now upload the custom config on the EC2 instance being used for benchmarking.

Provide a custom prompt/custom tokenizer for my benchmarking test

The instances section has an upload_files section for each instance where we can provide a list of local files and remote directory paths to place any custom file on an EC2 instance. This could be a tokenizer.json file, a custom prompt file, or a custom dataset. The example below shows how to upload a custom pricing.yml and a custom dataset to an EC2 instance.

instances:
- instance_type: g6e.2xlarge
  <<: *ec2_settings
  fmbench_config: 
  - fmbench:llama3/8b/config-ec2-llama3-8b-g6e-2xlarge.yml
  upload_files:
   - local: byo_dataset/custom.jsonl
     remote: /tmp/fmbench-read/source_data/
   - local: analytics/pricing.yml
     remote: /tmp/fmbench-read/configs/

See ec2_llama3.2-1b-cpu-byodataset.yml for an example config file. This file refers to the synthetic_data_large_prompts and a custom prompt file prompt_template_llama3_summarization.txt for a summarization task. You can edit the dataset file and the prompt template as per your requirements.

Benchmark multiple config files on the same EC2 instance

Often times we want to benchmark different combinations of parameters on the same EC2 instance, for example we may want to test tensor parallelism degree of 2, 4 and 8 for say Llama3.1-8b model on the same EC2 machine say g6e.48xlarge. Can do that easily with the orchestrator by specifying a list of config files rather than just a single config file as shown in the following example:

fmbench_config: 
- fmbench:llama3.1/8b/config-llama3.1-8b-g6e.48xl-tp-2-mc-max-djl.yml
- fmbench:llama3.1/8b/config-llama3.1-8b-g6e.48xl-tp-4-mc-max-djl.yml
- fmbench:llama3.1/8b/config-llama3.1-8b-g6e.48xl-tp-8-mc-max-djl.yml

The orchestrator would in this case first run benchmarking for the first file in the list, and then on the same EC2 instance run benchmarking for the second file and so on and so forth. The results folders and fmbench.log files for each of the runs is downloaded at the end when all config files for that instance have been processed.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the MIT-0 License - see the LICENSE file for details.