Enhancing deep learning model performance is essential for scalability and efficiency in the rapidly changing field of artificial intelligence. Intel has been in the forefront of creating frameworks and tools to improve AI models’ memory efficiency and speed of execution, especially with Intel Extension for PyTorch and Intel Extension for Transformers.
Comprehending the AI Stack
There are several layers in the AI stack, and each is essential to optimizing LLMs. The hardware layer, which consists of Intel Xeon CPUs, Intel Data Centre GPUs, Intel Arc GPUs, and Intel Gaudi AI accelerators, is fundamental.
The acceleration libraries, such as Intel oneAPI Collective Communications Library (oneCCL) and Intel oneAPI Deep Neural Network Library (oneDNN), sit above this layer and offer optimized kernels with Intel optimized instruction sets for effective processing. The highest layer is made up of resource-efficient frameworks such as PyTorch that interface with the hardware and libraries underneath to optimize model performance.
Important Optimization Methods
Optimizing operators is essential to improving LLM performance. Using enhanced instruction sets such as Intel enhanced Vector Extensions (Intel AVX), Intel Advanced Matrix Extensions (Intel AMX), and Intel Xe Matrix Extensions (Intel XMX), Intel replaces the default operation kernels with highly-optimized Intel oneDNN kernels. The accuracy-flexible design of this optimization ensures that applications can operate at maximum speed and precision by supporting a variety of data types, from FP32 to INT4.
Graph optimizations reduce the amount of memory accesses needed during computation, which further enhances efficiency. For example, memory access times can be reduced by combining layers (e.g., Conv+ReLU+Sum) with bandwidth-limited operations (e.g., activation functions, ReLU, or Tanh).
This method works especially well for models such as ResNet-50, where a large amount of processing time is dedicated to bandwidth-constrained tasks. Specific fusion methods, including as linear post-ops fusion and multi-head attention fusion, are used in the context of LLMs with Intel Extension for PyTorch in JIT/Torch script mode to improve performance.
Memory management is essential for maximizing LLM performance because they frequently require large amounts of memory. By pre-filling key/value pairs before to the onset of autoregressive decoding and utilising pre-allocated buffers throughout the decoding stage, the Segment KV Cache approach maximizes memory use.
This technique increases efficiency by lowering the requirement for in-the-moment memory changes. Similar to this, the Indirect Access KV Cache efficiently manages memory by utilising beam index history and pre-allocated buffers, which lowers the overhead related to memory access during inference.
Model compression uses quantization algorithms, which successively decrease weight and activation precision from FP32 to lower precision forms like INT8 or INT4. This reduction minimizes the size of the model, increases inference speed, and lowers the required for memory bandwidth. Smooth Quant is a post-training quantization technique that shifts the quantization difficulty from activations to weights. This allows for the preservation of model accuracy while mitigating activation outliers and optimizing hardware utilization.
A big part of optimization is also played by custom operators. The goal of weight-only quantization is to increase input and output activation precision by quantizing the model’s weights alone. With minimal influence on accuracy, this technique maximizes computational performance by utilising weight-only quantization-optimized bespoke GEMM (General Matrix Multiply) kernels. Performance can be further optimized by using Explicit SIMD (ESIMD) extensions, which provide more precise control over hardware features.
Intel Extension for PyTorch
APIs for implementing these optimizations on CPU and GPU based training and inference are provided by the Intel Extension for PyTorch. You may make sure that your models are optimized to operate well on Intel hardware by making use of these APIs. To make it easier for developers to execute these optimizations, the extension comes with environment configurations and scripts that are intended to maximize hardware utilization.
Another essential element of Intel’s optimization approach are the Intel Gaudi AI accelerators. Deep learning applications perform better because to the integration of PyTorch with the Intel Gaudi software suite, which effectively transfers neural network topologies onto Gaudi hardware. This integration also supports important kernel libraries and optimizations.
Intel Extension for Transformers
The Intel Extension for Transformers is another essential element that improves the Hugging Face Transformers library by incorporating new features and optimizing it for specific hardware. Model compression methods like Smooth Quant, weight-only quantization, and QLoRA (Quantized Low-Rank Adaptation) fine-tuning are supported by this update. Neural Chat, a platform for creating and implementing customizable chatbots with little to no code changes, is also introduced.
Several plugins for widely used pipelines, like audio processing and retrieval-augmented generation (RAG), can be integrated with Neural Chat. By integrating the required optimizations straight into the pipeline setup, it makes the deployment of optimized chatbots easier.
Neural Velocity and Dispersed Interpretation
Intel introduced Neural Speed, a dedicated library that simplifies LLM inference on Intel systems. Neural Speed leverages cutting-edge quantization methods to provide efficient inference, drawing inspiration from projects like as Llama CPP. It improves performance and memory efficiency by loading models with 4-bit or 8-bit precision by default, which makes it appropriate for a variety of AI applications.
DeepSpeed
These optimizations are further expanded across numerous nodes or GPUs via Intel’s support for distributed inference via DeepSpeed. DeepSpeed now supports Intel GPUs thanks to the Intel Extension for DeepSpeed. It includes the following parts:
- Implementation of the DeepSpeed Accelerator Interface
- Implementation of DeepSpeed op builder for XPU
- Code for DeepSpeed op builder kernel
With the help of oneCCL, this Intel-optimized extension distributes compute jobs well, lowering memory footprint and increasing throughput overall. Scaling AI applications across heterogeneous computer systems requires this capacity.
Utilising Optimizations in Real-World Applications
It’s actually very easy to implement these optimizations using Intel’s tools, as you can use the extensions for the PyTorch and Transformers frameworks. For example, Intel Extension for Transformers improves model compression methods such as weight-only and smooth quantization right inside the well-known Transformers API. By setting the quantization parameters and using the integrated APIs, you may optimize models with ease.
In a similar vein, the Intel Extension for Transformers and PyTorch offers an adaptable framework for optimizing deep learning models other than LLMs. This update provides GPU-centric capabilities like tensor parallelism and CPU optimizations like NUMA management and graph optimization’s to enable fine-tuning and deployment across a variety of hardware configurations.
In summary
You may significantly increase the effectiveness and performance of your AI models by utilising Intel’s extensive hardware stack, accelerated libraries, and optimized frameworks. These optimizations cut the energy and operating expenses associated with running large-scale AI applications in addition to improving computational performance and reducing latency.
Using the getting started samples from the Intel Extension for PyTorch and Intel Extension for Transformers, you can investigate these optimizations on the Intel Tiber Developer Cloud. You can make sure your LLMs are operating at optimal performance on Intel hardware by incorporating these strategies.
