Language Mannequin (LLM) shouldn’t be essentially the ultimate step in productionizing your Generative AI software. An typically forgotten, but essential a part of the MLOPs lifecycle is correctly load testing your LLM and guaranteeing it is able to stand up to your anticipated manufacturing site visitors. Load testing at a excessive stage is the apply of testing your software or on this case your mannequin with the site visitors it might expect in a manufacturing atmosphere to make sure that it’s performant.
Prior to now we’ve mentioned load testing conventional ML fashions utilizing open supply Python instruments akin to Locust. Locust helps seize common efficiency metrics akin to 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 result of their dimension and bigger compute necessities. Generally the RPS metric does probably not present probably the most correct image both as requests can significantly range relying on the enter to the LLM. For example you might need a question asking to summarize a big chunk of textual content and one other question that may require a one-word response.
Because of this 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, each 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 example as a phrase, sequence of phrases, or characters in essence.
What we’ll do on this article is discover how we will generate token based mostly metrics so we will 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 device particularly to benchmark totally different LLMs within the case that you’re evaluating many fashions or totally different deployment configurations or a mix of each.
Let’s get arms on! In case you are extra of a video based mostly learner be happy to comply with my corresponding YouTube video down under:
NOTE: This text assumes a primary understanding of Python, LLMs, and Amazon Bedrock/SageMaker. In case you are new to Amazon Bedrock please seek advice from my starter information right here. If you wish to be taught extra about SageMaker JumpStart LLM deployments seek advice from the video right here.
DISCLAIMER: I’m a Machine Studying Architect at AWS and my opinions are my very own.
Desk of Contents
- LLM Particular Metrics
- LLMPerf Intro
- Making use of LLMPerf to Amazon Bedrock
- Further Assets & Conclusion
LLM-Particular Metrics
As we briefly mentioned within the introduction with reference to LLM internet hosting, token based mostly metrics typically present a a lot better illustration of how your LLM is responding to totally different payload sizes or varieties of queries (summarization vs QnA).
Historically we’ve got all the time 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 pay attention to earlier than we get began with load testing:
- Time to First Token: That is the period it takes for the primary token to generate. That is particularly useful when streaming. For example when utilizing ChatGPT we begin processing data when the primary piece of textual content (token) seems.
- 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 various 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 akin to inter-token latency that may also be displayed as a part of the load checks. Be mindful the parameters that additionally affect these metrics embrace the anticipated enter and output token dimension. We particularly play with these parameters to get an correct understanding of how our LLM performs in response to totally different technology duties.
Now let’s check out a device that allows us to toggle these parameters and show the related metrics we’d like.
LLMPerf Intro
LLMPerf is constructed on prime of Ray, a preferred distributed computing Python framework. LLMPerf particularly leverages Ray to create distributed load checks the place we will simulate real-time manufacturing stage site visitors.
Be aware that any load-testing device can also be solely going to have the ability to generate your anticipated quantity of site visitors if the consumer machine it’s on has sufficient compute energy to match your anticipated load. For example as you scale the concurrency or throughput anticipated on your mannequin, you’d additionally need to scale the consumer machine(s) the place you’re operating your load take a look at.
Now particularly inside LLMPerf there’s just a few parameters which are uncovered which are tailor-made for LLM load testing as we’ve mentioned:
- Mannequin: That is the mannequin supplier and your hosted mannequin that you just’re working with. For our use-case it’ll be Amazon Bedrock and Claude 3 Sonnet particularly.
- LLM API: That is the API format through which the payload needs to be structured. We use LiteLLM which gives a standardized payload construction throughout totally different mannequin suppliers, thus simplifying the setup course of for us particularly if we need to take a look at totally different fashions hosted on totally different platforms.
- Enter Tokens: The imply enter token size, you may as well specify a regular deviation for this quantity.
- Output Tokens: The imply output token size, you may as well 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 way of their token_benchmark_ray.py script which we configure with our particular values. Let’s have a look now at how we will configure this particularly for Amazon Bedrock.
Making use of LLMPerf to Amazon Bedrock
Setup
For this instance we’ll be working in a SageMaker Basic Pocket book Occasion with a conda_python3 kernel and ml.g5.12xlarge occasion. Be aware that you just need to choose an occasion that has sufficient compute to generate the site visitors load that you just need to simulate. Be certain 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 selection which is LiteLLM on this case. With LiteLLM there’s help 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.decisions[0].message.content material
print(output)To work with Bedrock we configure the Mannequin ID to level in direction of Claude 3 Sonnet and cross in our immediate. The neat half with LiteLLM is that messages key has a constant format throughout mannequin suppliers.
Submit-execution right here we will deal with configuring LLMPerf for Bedrock particularly.
LLMPerf Bedrock Integration
To execute a load take a look at with LLMPerf we will merely use the supplied token_benchmark_ray.py script and cross 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 will 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-outputsOn this case we maintain the concurrency low, however be happy 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 it is best 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 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 will 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 various mannequin suppliers and run checks throughout these platforms. With this device you should utilize it holistically to determine the best mannequin and deployment stack on your use-case when used at scale.
Further Assets & Conclusion
Your complete code for the pattern will be discovered at this related Github repository. In case you additionally need to work with SageMaker endpoints you will discover a Llama JumpStart deployment load testing pattern right 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 will create a holistic take a look at with each parts.
As all the time thanks for studying and be happy to go away any suggestions and join with me on Linkedln and X.
