Azure OpenAI benchmarking tool

⚠️ Code in this repo is written for testing purposes and should not be used in production

⚠️ This project does not support testing more than 900 PTUs. For larger instances please use tools like JMeter/Gatling/etc for stress testing.

The Azure OpenAI Benchmarking tool is designed to aid customers in benchmarking their provisioned-throughput deployments. Provisioned throughput deployments provide a set amount of model compute. But determining the exact performance for you application is dependent on several variables such as: prompt size, generation size and call rate.

The benchmarking tool provides a simple way to run test traffic on your deploymnet and validate the throughput for your traffic workloads. The script will output key performance statistics including the average and 95th percentile latencies and utilization of the deployment.

You can use this tool to experiment with total throughput at 100% utilization across different traffic patterns for a Provisioned-Managed deployment type. These tests allow you to better optimize your solution design by adjusting the prompt size, generation size and PTUs deployed

Setup

Pre-requisites

1. An Azure OpenAI Service resource with a model deployed with a provisioned deployment (either Provisioned or Provisioned-Managed) deplyment type. For more information, see the resource deployment guide.
2. Your resource endpoint and access key. The script assumes the key is stored in the following environment variable: OPENAI_API_KEY. For more information on finding your endpoint and key, see the Azure OpenAI Quickstart.

Building and running

In an existing python environment:

$ pip install -r requirements.txt
$ python -m benchmark.bench load --help

Build a docker container:

$ docker build -t azure-openai-benchmarking .
$ docker run azure-openai-benchmarking load --help

General Guidelines

Consider the following guidelines when creating your benchmark tests

1. Ensure call characteristics match your production expectations. The number of calls per minute and total tokens you are able to process varies depending on the prompt size, generation size and call rate.
2. Run your test long enough to reach a stable state. Throttling is based on the total compute you have deployed and are utilizing. The utilization includes active calls. As a result you will see a higher call rate when ramping up on an unloaded deployment because there are no existing active calls being processed. Once your deplyoment is fully loaded with a utilzation near 100%, throttling will increase as calls can only be processed as earlier ones are completed. To ensure an accurate measure, set the duration long enough for the throughput to stabilize, especialy when running at or close to 100% utilization.
3. Consider whether to use a retry strategy, and the effect of throttling on the resulting stats. There are careful considerations when selecting a retry strategy, as the resulting latency statistics will be effected if the resource is pushed beyond it's capacity and to the point of throttling.

• When running a test with retry=none, any throttled request will be treated as throttled and a new request will be made to replace it, with the start time of the replacement request being reset to a newer time. If the resource being tested starts returning 429s, then any latency metrics from this tool will only represent the values of the final successful request, without also including the time that was spent retrying to resource until a successful response was received (which may not be representative of the real-world user experience). This setting should be used when the workload being tested results is within the resource's capacity and no throttling occurs, or where you are looking to understand what percentage of requests to a PTU instance might need to be diverted to a backup resource, such as during periods of peak load which require more throughput than the PTU resource can handle.
• When running a test with retry=exponential, any failed or throttled request will be retried with exponential backoff, up to a max of 60 seconds. While it is always recommended to deploy backup AOAI resources for use-cases that will experience periods of high load, this setting may be useful for trying to simulate a scenario where no backup resource is available, and where throttled or failed requests must still be fulfilled by the resource. In this case, the TTFT and e2e latency metrics will represent the time from the first throttled request to the time that the final request was successful, and may be more reflective of the total time that an end user could spend waiting for a response, e.g. in a chat application. Use this option in situations where you want to understand the latency of requests which are throttled and need to be retried on the same resource, and the how the total latency of a request is impacted by multiple request retries.
• As a practical example, if a PTU resource is tested beyond 100% capacity and starts returning 429s:
  • With retry=none the TTFT and e2e latency statistics will remain stable (and very low), since only the successful requests will be included in the metrics. Number of throttled requests will be relatively high.
  • With retry=exponential, the TTFT/e2e latency metrics will increase (potentially up to the max of 60 seconds), while the number of throttled requests will remain lower (since a request is only treated as throttled after 60 seconds, regardless of how many attempts were made within the retry period).
  • Total throughput values (RPM, TPM) may be lower when retry=none if rate limiting is applied.
• As a best practice, any PTU resource should be deployed with a backup PayGO resource for times of peak load. As a result, any testing should be conducted with the values suggested in the AOAI capacity calculator (within the AI Azure Portal) to ensure that throttling does not occur during testing.

Usage examples

Common Scenarios:

The table below provides an example prompt & generation size we have seen with some customers. Actual sizes will vary significantly based on your overall architecture For example,the amount of data grounding you pull into the prompt as part of a chat session can increase the prompt size significantly.

Scenario	Prompt Size	Completion Size	Calls per minute	Provisioned throughput units (PTU) required
Chat	1000	200	45	200
Summarization	7000	150	7	100
Classification	7000	1	24	300

Or see the pre-configured shape-profiles below.

Run samples

During a run, statistics are output every second to stdout while logs are output to stderr. Some metrics may not show up immediately due to lack of data.

Run load test at 60 RPM with exponential retry back-off

$ python -m benchmark.bench load \
    --deployment gpt-4 \
    --rate 60 \
    --retry exponential \
    https://myaccount.openai.azure.com

2023-10-19 18:21:06 INFO     using shape profile balanced: context tokens: 500, max tokens: 500
2023-10-19 18:21:06 INFO     warming up prompt cache
2023-10-19 18:21:06 INFO     starting load...
2023-10-19 18:21:06 rpm: 1.0   requests: 1     failures: 0    throttled: 0    ctx tpm: 501.0  gen tpm: 103.0  ttft avg: 0.736  ttft 95th: n/a    tbt avg: 0.088  tbt 95th: n/a    e2e avg: 1.845  e2e 95th: n/a    util avg: 0.0%   util 95th: n/a   
2023-10-19 18:21:07 rpm: 5.0   requests: 5     failures: 0    throttled: 0    ctx tpm: 2505.0 gen tpm: 515.0  ttft avg: 0.937  ttft 95th: 1.321  tbt avg: 0.042  tbt 95th: 0.043  e2e avg: 1.223 e2e 95th: 1.658 util avg: 0.8%   util 95th: 1.6%  
2023-10-19 18:21:08 rpm: 8.0   requests: 8     failures: 0    throttled: 0    ctx tpm: 4008.0 gen tpm: 824.0  ttft avg: 0.913  ttft 95th: 1.304  tbt avg: 0.042  tbt 95th: 0.043  e2e avg: 1.241 e2e 95th: 1.663 util avg: 1.3%   util 95th: 2.6% 

Load test with custom request shape

$ python -m benchmark.bench load \
    --deployment gpt-4 \
    --rate 1 \
    --shape custom \
    --context-tokens 1000 \
    --max-tokens 500 \
    https://myaccount.openai.azure.com

Obtain number of tokens for input context

tokenize subcommand can be used to count number of tokens for a given input. It supports both text and json chat messages input.

$ python -m benchmark.bench tokenize \
    --model gpt-4 \
    "this is my context"
tokens: 4

Alternatively you can send your text via stdin:

$ cat mychatcontext.json | python -m benchmark.bench tokenize \
    --model gpt-4
tokens: 65

Configuration Option Details

Shape profiles

The tool generates synthetic requests using random words according to the number of context tokens in the shape profile requested. In addition, to avoid any engine optimizations, each prompt is prefixed with a random prefix to force engine to run a full request processing for each request without any optimization. This ensures that the results observed while running the tool are the worst case scenario for given traffic shape.

The tool supports four different shape profiles via command line option --shape-profile:

profile	description	context tokens	max tokens
balanced	[default] Balanced count of context and generation tokens. Should be representative of typical workloads.	500	500
context	Represents workloads with larger context sizes compared to generation. For example, chat assistants.	2000	200
generation	Represents workloads with larger generation and smaller contexts. For example, question answering.	500	1000
custom	Allows specifying custom values for context size (--context-tokens) and max generation tokens (--max-tokens).

Output fields

field	description	sliding window	example
time	Time offset in seconds since the start of the test.	no	120
rpm	Successful Requests Per Minute. Note that it may be less than --rate as it counts completed requests.	yes	12
processing	Total number of requests currently being processed by the endpoint.	no	100
completed	Total number of completed requests.	no	100
failures	Total number of failed requests out of requests.	no	100
throttled	Total number of throttled requests out of requests.	no	100
requests	Deprecated in favor of completed field (output values of both fields are the same)	no	1233
ctx_tpm	Number of context Tokens Per Minute.	yes	1200
gen_tpm	Number of generated Tokens Per Minute.	yes	156
ttft_avg	Average time in seconds from the beginning of the request until the first token was received.	yes	0.122
ttft_95th	95th percentile of time in seconds from the beginning of the request until the first token was received.	yes	0.130
tbt_avg	Average time in seconds between two consequitive generated tokens.	yes	0.018
tbt_95th	95th percentail of time in seconds between two consequitive generated tokens.	yes	0.021
e2e_avg	Average end to end request time.	yes	1.2
e2e_95th	95th percentile of end to end request time.	yes	1.5
util_avg	Average deployment utilization percentage as reported by the service.	yes	89.3%
util_95th	95th percentile of deployment utilization percentage as reported by the service.	yes	91.2%

Note: Prior to the benchmarking run reaching aggregation-window in elapsed time, all sliding window stats will be calculated over a dynamic window, equal to the time elapsed since starting the test. This ensures RPM/TPM stats are relatively accurate prior to the test reaching completion, including when a test ends early due to reaching the request limit.

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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