Add InfiniteBench for long context benchmarking

[iankur]

Motivation

This PR adds support for eval on a long context benchmark, InfiniteBench. See #1273 for more context.

Modifications

Following the discussion in #1273, it currently adds code from TensorRT-LLM repo (link) to load the data, create prompts and compute scores. Following are the sample outputs for both cases using gradientai/Llama-3-8B-Instruct-Gradient-1048k with maximum input length of ~130K. Please check readme for more details and instructions on how to run both the benchmarks. Currently, predictions are different (see below) which I will try to fix.

SGLang

{"question_id": 0, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 71432.", "ground_truth": ["71432"]}
{"question_id": 1, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 69079.", "ground_truth": ["69079"]}
{"question_id": 2, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 89415.", "ground_truth": ["89415"]}
{"question_id": 3, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 61734.", "ground_truth": ["61734"]}
{"question_id": 4, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 40204.", "ground_truth": ["40204"]}
{"question_id": 5, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 80723.", "ground_truth": ["80723"]}
{"question_id": 6, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 55058.", "ground_truth": ["55058"]}
{"question_id": 7, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 16783.", "ground_truth": ["16783"]}
{"question_id": 8, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 15951.", "ground_truth": ["15951"]}
{"question_id": 9, "model_id": "gradientai/Llama-3-8B-Instruct-Gradient-1048k", "prediction": " 52933.", "ground_truth": ["52933"]}

TensorRT-LLM

{"id": 0, "prediction": " 71432.", "ground_truth": ["71432"], "input_lengths": [125339]}
{"id": 1, "prediction": " 69079.", "ground_truth": ["69079"], "input_lengths": [125339]}
{"id": 2, "prediction": " 89415.", "ground_truth": ["89415"], "input_lengths": [125339]}
{"id": 3, "prediction": " 61734.", "ground_truth": ["61734"], "input_lengths": [125339]}
{"id": 4, "prediction": " 40204.", "ground_truth": ["40204"], "input_lengths": [125339]}
{"id": 5, "prediction": " 80723.", "ground_truth": ["80723"], "input_lengths": [125339]}
{"id": 6, "prediction": " 55058.", "ground_truth": ["55058"], "input_lengths": [125339]}
{"id": 7, "prediction": " 16783. Remember it", "ground_truth": ["16783"], "input_lengths": [125339]}
{"id": 8, "prediction": " 15951.", "ground_truth": ["15951"], "input_lengths": [125339]}
{"id": 9, "prediction": " 52933.", "ground_truth": ["52933"], "input_lengths": [125339]}

Checklist

• Format your code according to the Contributor Guide.
• Add unit tests as outlined in the Contributor Guide.
• Update documentation as needed, including docstrings or example tutorials.

[zhyncs]

Nice work! May we combine these scripts just to one? Something like this

sglang/python/sglang/bench_serving.py

Line 519 in 641b7d0

SHAREGPT_URL = "https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json"

Implement the process of downloading files into a script to make it more convenient for users.

[zhyncs]

Additionally, the section about TensorRT LLM is very good! Would you be willing to help improve this custom task script to make it easier to test TensorRT LLM?
https://github.com/sgl-project/sglang/blob/main/test/srt/experiment_runner.py
ref #2407
If considering doing it, it can be implemented in another PR. Thanks!

[zhyncs]

close #1273

[zhyncs]

gradientai/Llama-3-8B-Instruct-Gradient-1048k GradientAI LOL your previous work. cc @michaelfeil

[iankur]

@zhyncs

Implement the process of downloading files into a script to make it more convenient for users.

Sounds good, I will merge the downloading script for sglang, we can keep the downloading script for tensorrt.

I will also work on the custom task script PR. I am traveling, so it may take some time but will try to do it asap.

[liangan1]

can you add more descriptions about the "--start-idx" and "--end-idx" ?

[iankur]

I removed these arguments, which were borrowed from tensorrt eval script, and added num-samples with description.

[merrymercy]

Is this ready to be merged?
We can have this first and then add this to CI in the next PR.

[merrymercy]

cc @iankur and @zhyncs . Ready to merge this first part?