Today's gen-AI workflows that involve multiple ML model calls, tool/API calls, data retrieval, or generic code execution are often tuned manually in an ad-hoc way that is both time-consuming and error-prone. In this paper, we propose a systematic approach for automatically tuning gen-AI workflows. Our key insight is that gen-AI workflows can benefit from structure, operator, and prompt changes, but unique properties of gen-AI workflows require new optimization techniques. We propose AdaSeek, an adaptive hierarchical search algorithm for autotuning gen-AI workflows. AdaSeek organizes workflow tuning methods into different layers based on the user-specified total search budget and distributes the budget across different layers based on the complexity of each layer. During its hierarchical search, AdaSeek redistributes the search budget from less useful to more promising tuning configurations based on workflow-level evaluation results. We implement AdaSeek in a workflow autotuning framework called Cognify and evaluate Cognify using six types of workflows such as RAG-based QA and text-to-SQL transformation. Overall, Cognify improves these workflows' generation quality by up to 2.8x, reduces execution monetary cost by up to 10x, and reduces end-to-end latency by 2.7x.

Large language models are increasingly integrated with external tools and APIs like ChatGPT plugins to extend their capability beyond language-centric tasks. However, today's LLM inference systems are designed for standalone LLMs. They treat API calls as new requests, causing unnecessary recomputation of already computed contexts, which accounts for 37-40% of total model forwarding time. This paper presents APIServe, the first LLM inference framework targeting API-augmented LLMs. APISERVE minimizes the GPU resource waste caused by API calls and dedicates saved memory for serving more requests. APISERVE improves the overall serving throughput by 1.6x and completes 2x more requests per second compared to the state-of-the-art LLM inference systems.

This approach is also called autoregressive decoding because each The high computational and memory requirements of generative generated token is also used as input for generating future large language models (LLMs) make it challenging tokens. This dependency between tokens is crucial for many to serve them quickly and cheaply. This paper introduces NLP tasks that require preserving the order and context of the SpecInfer, an LLM serving system that accelerates generative generated tokens, such as text completion [53]. LLM inference with speculative inference and token tree Existing LLM systems generally use an incremental decoding verification. A key insight behind SpecInfer is to combine approach to serving a request where the system computes various collectively boost-tuned small language models to the activations for all prompt tokens in a single step and then jointly predict the LLM's outputs; the predictions are organized iteratively decodes one new token using the input prompt as a token tree, whose nodes each represent a candidate and all previously generated tokens. This approach respects token sequence. The correctness of all candidate token sequences data dependencies between tokens, but achieves suboptimal represented by a token tree is verified against the runtime performance and limited GPU utilization, since the LLM in parallel using a novel tree-based parallel decoding degree of parallelism within each request is greatly limited in mechanism. SpecInfer uses an LLM as a token tree verifier the incremental phase. In addition, the attention mechanism of instead of an incremental decoder, which significantly Transformer [46] requires accessing the keys and values of all reduces the end-to-end latency and computational requirement previous tokens to compute the attention output of a new token.