Load-Testing LLMs Using LLMPerf | Towards Data Science

Language Mannequin (LLM) just isn’t essentially the ultimate step in productionizing your Generative AI utility. An typically forgotten, but essential a part of the MLOPs lifecycle is correctly load testing your LLM and guaranteeing it is able to face up to your anticipated manufacturing site visitors. Load testing at a excessive stage is the follow of testing your utility or on this case your mannequin with the site visitors it could expect in a manufacturing surroundings to make sure that it’s performant.

Prior to now we’ve mentioned load testing traditional ML models utilizing open supply Python instruments reminiscent of Locust. Locust helps seize normal efficiency metrics reminiscent of requests per second (RPS) and latency percentiles on a per request foundation. Whereas that is efficient with extra conventional APIs and ML fashions it doesn’t seize the total story for LLMs. 

LLMs historically have a a lot decrease RPS and better latency than conventional ML fashions as a consequence of their measurement and bigger compute necessities. Basically the RPS metric does not likely present probably the most correct image both as requests can significantly fluctuate relying on the enter to the LLM. As an example you might need a question asking to summarize a big chunk of textual content and one other question which may require a one-word response. 

That is why tokens are seen as a way more correct illustration of an LLM’s efficiency. At a excessive stage a token is a bit of textual content, every time an LLM is processing your enter it “tokenizes” the enter. A token differs relying particularly on the LLM you’re utilizing, however you’ll be able to think about it as an illustration as a phrase, sequence of phrases, or characters in essence.

What we’ll do on this article is discover how we are able to generate token primarily based metrics so we are able to perceive how your LLM is acting from a serving/deployment perspective. After this text you’ll have an concept of how one can arrange a load-testing software particularly to benchmark completely different LLMs within the case that you’re evaluating many fashions or completely different deployment configurations or a mix of each.

Let’s get fingers on! In case you are extra of a video primarily based learner be at liberty to comply with my corresponding YouTube video down under:

NOTE: This text assumes a fundamental understanding of Python, LLMs, and Amazon Bedrock/SageMaker. In case you are new to Amazon Bedrock please confer with my starter information here. If you wish to study extra about SageMaker JumpStart LLM deployments confer with the video here.

DISCLAIMER: I’m a Machine Studying Architect at AWS and my opinions are my very own.

Desk of Contents

1. LLM Particular Metrics
2. LLMPerf Intro
3. Making use of LLMPerf to Amazon Bedrock
4. Extra Sources & Conclusion

LLM-Particular Metrics

As we briefly mentioned within the introduction with reference to LLM internet hosting, token primarily based metrics typically present a a lot better illustration of how your LLM is responding to completely different payload sizes or kinds of queries (summarization vs QnA). 

Historically we now have at all times tracked RPS and latency which we’ll nonetheless see right here nonetheless, however extra so at a token stage. Listed below are a number of the metrics to concentrate on earlier than we get began with load testing:

1. Time to First Token: That is the period it takes for the primary token to generate. That is particularly helpful when streaming. As an example when utilizing ChatGPT we begin processing info when the primary piece of textual content (token) seems.
2. Complete Output Tokens Per Second: That is the full variety of tokens generated per second, you’ll be able to consider this as a extra granular different to the requests per second we historically monitor.

These are the foremost metrics that we’ll deal with, and there’s just a few others reminiscent of inter-token latency that can even be displayed as a part of the load exams. Remember the parameters that additionally affect these metrics embrace the anticipated enter and output token measurement. We particularly play with these parameters to get an correct understanding of how our LLM performs in response to completely different era duties. 

Now let’s check out a software that permits us to toggle these parameters and show the related metrics we want.

LLMPerf Intro

LLMPerf is constructed on high of Ray, a well-liked distributed computing Python framework. LLMPerf particularly leverages Ray to create distributed load exams the place we are able to simulate real-time manufacturing stage site visitors. 

Word that any load-testing software can be solely going to have the ability to generate your anticipated quantity of site visitors if the shopper machine it’s on has sufficient compute energy to match your anticipated load. As an example as you scale the concurrency or throughput anticipated in your mannequin, you’d additionally wish to scale the shopper machine(s) the place you’re operating your load take a look at.

Now particularly inside LLMPerf there’s just a few parameters which can be uncovered which can be tailor-made for LLM load testing as we’ve mentioned:

• Mannequin: That is the mannequin supplier and your hosted mannequin that you simply’re working with. For our use-case it’ll be Amazon Bedrock and Claude 3 Sonnet particularly.
• LLM API: That is the API format during which the payload ought to be structured. We use LiteLLM which supplies a standardized payload construction throughout completely different mannequin suppliers, thus simplifying the setup course of for us particularly if we wish to take a look at completely different fashions hosted on completely different platforms.
• Enter Tokens: The imply enter token size, it’s also possible to specify a regular deviation for this quantity.
• Output Tokens: The imply output token size, it’s also possible to specify a regular deviation for this quantity.
• Concurrent Requests: The variety of concurrent requests for the load take a look at to simulate.
• Take a look at Period: You may management the period of the take a look at, this parameter is enabled in seconds.

LLMPerf particularly exposes all these parameters by their token_benchmark_ray.py script which we configure with our particular values. Let’s have a look now at how we are able to configure this particularly for Amazon Bedrock.

Making use of LLMPerf to Amazon Bedrock

Setup

For this instance we’ll be working in a SageMaker Classic Notebook Instance with a conda_python3 kernel and ml.g5.12xlarge occasion. Word that you simply wish to choose an occasion that has sufficient compute to generate the site visitors load that you simply wish to simulate. Be sure that you even have your AWS credentials for LLMPerf to entry the hosted mannequin be it on Bedrock or SageMaker.

LiteLLM Configuration

We first configure our LLM API construction of alternative which is LiteLLM on this case. With LiteLLM there’s assist throughout numerous mannequin suppliers, on this case we configure the completion API to work with Amazon Bedrock:

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = "Enter your entry key ID"
os.environ["AWS_SECRET_ACCESS_KEY"] = "Enter your secret entry key"
os.environ["AWS_REGION_NAME"] = "us-east-1"

response = completion(
    mannequin="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[{ "content": "Who is Roger Federer?","role": "user"}]
)
output = response.selections[0].message.content material
print(output)

To work with Bedrock we configure the Mannequin ID to level in the direction of Claude 3 Sonnet and move in our immediate. The neat half with LiteLLM is that messages key has a constant format throughout mannequin suppliers.

Submit-execution right here we are able to deal with configuring LLMPerf for Bedrock particularly.

LLMPerf Bedrock Integration

To execute a load take a look at with LLMPerf we are able to merely use the supplied token_benchmark_ray.py script and move within the following parameters that we talked of earlier:

• Enter Tokens Imply & Normal Deviation
• Output Tokens Imply & Normal Deviation
• Max variety of requests for take a look at
• Period of take a look at
• Concurrent requests

On this case we additionally specify our API format to be LiteLLM and we are able to execute the load take a look at with a easy shell script like the next:

%%sh
python llmperf/token_benchmark_ray.py 
    --model bedrock/anthropic.claude-3-sonnet-20240229-v1:0 
    --mean-input-tokens 1024 
    --stddev-input-tokens 200 
    --mean-output-tokens 1024 
    --stddev-output-tokens 200 
    --max-num-completed-requests 30 
    --num-concurrent-requests 1 
    --timeout 300 
    --llm-api litellm 
    --results-dir bedrock-outputs

On this case we maintain the concurrency low, however be at liberty to toggle this quantity relying on what you’re anticipating in manufacturing. Our take a look at will run for 300 seconds and publish period you need to see an output listing with two information representing statistics for every inference and in addition the imply metrics throughout all requests within the period of the take a look at.

We are able to make this look a bit of neater by parsing the abstract file with pandas:

import json
from pathlib import Path
import pandas as pd

# Load JSON information
individual_path = Path("bedrock-outputs/bedrock-anthropic-claude-3-sonnet-20240229-v1-0_1024_1024_individual_responses.json")
summary_path = Path("bedrock-outputs/bedrock-anthropic-claude-3-sonnet-20240229-v1-0_1024_1024_summary.json")

with open(individual_path, "r") as f:
    individual_data = json.load(f)

with open(summary_path, "r") as f:
    summary_data = json.load(f)

# Print abstract metrics
df = pd.DataFrame(individual_data)
summary_metrics = {
    "Mannequin": summary_data.get("mannequin"),
    "Imply Enter Tokens": summary_data.get("mean_input_tokens"),
    "Stddev Enter Tokens": summary_data.get("stddev_input_tokens"),
    "Imply Output Tokens": summary_data.get("mean_output_tokens"),
    "Stddev Output Tokens": summary_data.get("stddev_output_tokens"),
    "Imply TTFT (s)": summary_data.get("results_ttft_s_mean"),
    "Imply Inter-token Latency (s)": summary_data.get("results_inter_token_latency_s_mean"),
    "Imply Output Throughput (tokens/s)": summary_data.get("results_mean_output_throughput_token_per_s"),
    "Accomplished Requests": summary_data.get("results_num_completed_requests"),
    "Error Charge": summary_data.get("results_error_rate")
}
print("Claude 3 Sonnet - Efficiency Abstract:n")
for okay, v in summary_metrics.objects():
    print(f"{okay}: {v}")

The ultimate load take a look at outcomes will look one thing like the next:

As we are able to see we see the enter parameters that we configured, after which the corresponding outcomes with time to first token(s) and throughput with reference to imply output tokens per second.

In a real-world use case you would possibly use LLMPerf throughout many alternative mannequin suppliers and run exams throughout these platforms. With this software you need to use it holistically to determine the suitable mannequin and deployment stack in your use-case when used at scale.

Extra Sources & Conclusion

Your entire code for the pattern could be discovered at this related Github repository. In the event you additionally wish to work with SageMaker endpoints yow will discover a Llama JumpStart deployment load testing pattern here.

All in all load testing and analysis are each essential to making sure that your LLM is performant towards your anticipated site visitors earlier than pushing to manufacturing. In future articles we’ll cowl not simply the analysis portion, however how we are able to create a holistic take a look at with each parts.

As at all times thanks for studying and be at liberty to depart any suggestions and join with me on Linkedln and X.