High-Performance Inference with Nvidia's Cutlass Library

Tyler demonstrates how Cutlass enhances high-performance quantized inference in linear algebra operations for BLM models.

### Optimizing Inference with Cutlass: High-Performance Quantized Operations in BLM
* 🧠 Tyler discusses the use of Cutlass, an Nvidia library, which provides **modular routines for high-performance linear algebra operations**, particularly in **quantized inference for BLM**.
* 🎯 The presentation sets expectations by outlining the focus on **activation quantization**, **performance metrics**, and the significance of **speeding up linear layers** while avoiding **ultra-low precision quantization** and the inner workings of **Cutlass**.
* 📊 A breakdown of the individual layers in Meta Llama 38B compares fp16 and fp8 performance, highlighting that **dense gems (linear layers)** are the main area of improvement, especially as **batch sizes increase**.
* 🔍 The talk emphasizes the importance of **quantizing both weights and activations (W8 A8)** for effective speedups, as weight-only quantization is **insufficient for inference efficiency**.
* 📈 The roofline model is introduced to explain performance limits based on computation and data movement rates, illustrating that the **arithmetic intensity of the algorithm** affects overall performance.
* 💡 Cutlass's epilog fusion technique is highlighted as a key feature, allowing for **reduced data movement** by applying operations to outputs **right before they are written to GPU memory**.
* ⚙ Future plans for BLM and Cutlass include **Hopper-optimized mixed precision kernels** and integrating additional kernels to **enhance performance further**.
* 👥 Tyler acknowledges contributions from the team and encourages the audience to explore the BLM project for **easy-to-use interfaces** in producing models with **activation quantization**.

### Advancements in Vision Language Models and Hardware Support
* 🔊 The Ultra box model marked a significant milestone as the **first audio model**, with ongoing enhancements in vision language models, including support for **GGF models** and **encoder-decoder models**.
* 💻 Recent updates have improved hardware support, particularly for **TPU and Intel CPUs**, and introduced **multi-step scheduling** to enhance performance.
* 🚀 The release of version 0.6 has **stabilized the multi-step scheduler** and **expanded support for AMD GPUs**, with Nvidia contributing to **accelerated FP8 KV cache performance**.
* 🔍 Model support has been broadened, now accommodating various bits and bytes checkpoints from Hugging Face, including notable models like **X01 and Granite**.
* 📈 The tools API for OpenAI interface has been finalized, and features like **chunk prefill** and **prefix caching** have been integrated for **improved prompting**.
* ⚙ Contributors have focused on **reducing CPU contention**, enhancing **asynchronous output processing**, and optimizing **batch scheduling** to **increase throughput and reduce latency**.
* 📊 The latest performance metrics indicate **significant improvements in throughput and latency**, positioning VM **competitively against other inference servers**.
* 🤝 The community is encouraged to participate in ongoing development, with **open invitations for contributions and feedback** to enhance the project further.

vLLM Office Hours - Using NVIDIA CUTLASS for High-Performance Inference - September 05, 2024

Neural Magic

High-Performance Quantized Inference using Cutlass

Cutlass, an Nvidia collection of modular routines, is used to achieve high-performance quantized inference in BLM.

The focus is on activation quantization, speeding up linear layers, and handling scales and zero points in quantization.

The talk covers different types of quantization, including per tensor, per token, and per channel, as well as symmetric and asymmetric quantization, while also discussing the efficiency of linear layers and the impact of batch size.

Dense GEMs are executed in VLM linear layers, with large weight matrices and a variable number of tokens processed at once.

Quantizing both weights and activations (W8 A8) is crucial for improving performance, as weight-only quantization is insufficient for speedups.

The roofline model helps to understand the relationship between computation rate, data movement, and arithmetic intensity, highlighting the importance of larger matrices for higher arithmetic intensity.

Understanding the Roofline Plot in Algorithm Performance

The roofline plot illustrates the relationship between an algorithm's performance (in teraflops) and its arithmetic intensity (flops per byte).

It shows upper bounds on performance based on the algorithm's properties, with lines representing maximum compute and bandwidth.

The plot highlights the impact of factors like prefill size, batch size, and system QPS rate on arithmetic intensity, suggesting that techniques like chunked prefill and speculative decoding can help increase token processing efficiency.

The Role of Cutlass in Quantized Operations

Cutlass is a library that provides CUDA C++ template abstractions for high-performance linear algebra, allowing users to build libraries from modular pieces.

Epilog fusion, a key feature of Cutlass, reduces data movement by applying operations to operands in fast memory, particularly during the output stage.

Symmetric quantization is used in the gem multiplication, with scales applied to the output during the epilogue, while handling various cases such as per tensor and dynamic per token.

Optimizing Tensor Operations with Cutlass

The outer product is performed during the epilog to avoid pre-computation, resulting in efficient tensor operations.

Cutlass's implementation of per tensor and per channel GEMs shows no performance loss, even with dynamic scaling.

Future plans include optimizing mixed precision kernels and integrating additional Cutlass-based kernels into the VLM project.

Recent Improvements in Vision Language Models

The Ultra box audio model has led to continuous improvements in vision language models, with added support for GGF models and encoder-decoder models.

6, focuses on performance improvements, hardware support expansion, and model support for various checkpoints.

Key features include a new chat method, tools API for OpenAI interface, and the ability to enable chunk prefill and prefix caching together.

Improving GPU Utilization and Model Training

The new approach schedules end steps simultaneously, reducing synchronization with the GPU and increasing throughput.

Asynchronous output processing allows for better GPU efficiency by minimizing pauses for output handling.

The implementation of conditional testing and accountability for test upkeep aims to address stability issues on the main branch.

Fine-Tuning Data Sets and Model Optimization

Openly available fine-tuning data sets are crucial for optimizing token budgets and model calibration.

The format of prompts should align with the model's training schema for effective use.

The speaker is exploring unconventional ways to optimize LLM inference times within the Databricks environment and is interested in using the async LLM engine for backend access and asynchronous scheduling.

To enhance throughput when serving, focus on maximizing GPU memory utilization for KV cache and batching requests in parallel.

Enable chunk prefill to reduce output token interruptions and consider using pipeline parallelism to split workloads across multiple nodes.

Running multiple model copies on a single GPU by lowering memory utilization can also improve efficiency.

Changes in Transformers and BLM Contributions

Transformers introduced a breaking change, no longer defaulting to chat templates for models without them, which can lead to bad behavior.

Users are advised to use the completions API instead of the chat API for models without a chat template.

The GGF format's chat template extraction at runtime and the need for improved user experience are also discussed, along with opportunities for contributing to BLM and upcoming events.