add: Dynamic Sonnet

README.md

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+---
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: 1k
+    path: "dynamic_sonnet_llama_2_prefix_256_max_1024_1024_sampled.parquet"
+  - split: 2k
+    path: "dynamic_sonnet_llama_2_prefix_512_max_2048_1024_sampled.parquet"
+  - split: 4k
+    path: "dynamic_sonnet_llama_2_prefix_1024_max_4096_1024_sampled.parquet"
+  - split: 8k
+    path: "dynamic_sonnet_llama_2_prefix_2048_max_8192_1024_sampled.parquet"
+---
+
+# Dynamic Sonnet - Llama2
+*Curated dataset for benchmarking LLM serving systems*
+
+![plot](distribution.png)
+
+In real-world service scenarios, each request comes with varying input token lengths.
+Some requests generate only a few tokens, while others produce a significant number.
+Traditional fixed-length benchmarks fail to capture this variability, making it difficult to accurately assess real-world throughput performance.
+This dynamic nature of input token lengths is crucial as it directly affects key features of LLM serving systems, such as continuous batching, which are essential for optimal performance.
+
+To address this challenge, we introduce ***Dynamic Sonnet***—a dataset designed specifically for benchmarking LLM serving systems under realistic conditions.
+***Dynamic Sonnet*** comprises four subsets: 1k, 2k, 4k, and 8k. Each subset is carefully curated to have an average token length of 512, 1k, 3k, and 7k, respectively.
+This variability in token length within the dataset allows for a more accurate and comprehensive evaluation of LLM serving systems in environments that mirror real-world usage.
+
+Furthermore, in real-world scenarios, requests often share common prefixes. Advanced systems can leverage this by caching these prefixes to boost performance. 
+***Dynamic Sonnet*** simulates this behavior by incorporating a common prefix that constitutes approximately 25% of the maximum length in each subset (N/4 for an Nk subset).
+This design allows for more realistic benchmarking of systems that optimize for such efficiencies.
+
+## Details
+
+The Dynamic Sonnet dataset consists of five columns: `id`, `system_prompt`, `user_prompt`, `formatted_input` and `tok_inputs`
+
+* `id`: A unique identifier (index) for each prompt
+* `system_prompt`: A common prefix that instructs the agent to select specific lines from the following text
+* `user_prompt`: The lines selected from Shakespeare's sonnets
+* `formatted_input`: The prompt(`system_prompt`+`user_prompt`) formatted according to a specific chat template
+* `tok_inputs`: The tokenized version of the `formatted_input`
+
+## Usage
+
+To benchmark with ***Dynamic Sonnet***, users can pass the token IDs (tok_inputs) directly to the LLM serving system.
+For benchmarking an OpenAI-compatible system, users can concatenate the `system_prompt` and `user_prompt`, and then send a request to `v1/chat/completions` endpoint, using the concatenated result as the request body.

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