Added blog for ignite

README.md

@@ -1,7 +1,3 @@
-# create-svelte
-
-Everything you need to build a Svelte project, powered by [`create-svelte`](https://github.com/sveltejs/kit/tree/master/packages/create-svelte).
-
 ## Creating a project
 
 If you're seeing this, you've probably already done this step. Congrats!

src/routes/blogs/+page.svelte

@@ -6,6 +6,8 @@
 	import anime from 'animejs';
 	import { onMount } from 'svelte';
 	import ImageBlogs from '../../images/undraw/image_blogs.svelte';
+	import HFImage from '../../images/blogs/hugging-face-blog-img.png';
+	import LlamaImage from '../../images/blogs/accelerating-llama-2/Figure1-LLaMA-2-7B-E2E-Throughput.png';
 	onMount(() => {
 		anime({
 			targets: '.border-primary',
@@ -21,21 +23,43 @@
 		});
 	});
 	let featuredblog = [
+		{
+			title: 'Accelerating LLaMA-2 Inference with ONNX Runtime',
+			date: 'November 14th, 2023',
+			blurb: 'Learn how ONNX Runtime can speed up LLaMA-2 inference by up to 4.5X',
+			link: 'blogs/accelerating-llama-2',
+			image: LlamaImage,
+			imgalt: 'LLaMA-2 e2e throughput'
+		},
 		{
 			title: 'Run PyTorch models on the edge',
 			date: 'October 12th, 2023',
 			blurb:
 				'Everything you need to know about running PyTorch models on the edge with ONNX Runtime.',
 			link: 'blogs/pytorch-on-the-edge',
-			image: 'https://onnxruntime.ai/_app/immutable/assets/pytorch-on-the-edge-with-ort.cdaa9c84.png'
+			image:
+				'https://onnxruntime.ai/_app/immutable/assets/pytorch-on-the-edge-with-ort.cdaa9c84.png',
+				imgalt: 'Run PyTorch models on the edge'
 		},
+		{
+			title: 'Accelerating over 130,000 Hugging Face models with ONNX Runtime',
+			date: 'October 4th, 2023',
+			blurb:
+				'Learn more on how ONNX Runtime helps users accelerate open source machine learning models from Hugging Face.',
+			link: 'https://cloudblogs.microsoft.com/opensource/2023/10/04/accelerating-over-130000-hugging-face-models-with-onnx-runtime/',
+			image: HFImage,
+			imgalt: 'Hugging Face models with ONNX Runtime'
+		}
+	];
+	let blogs = [
 		{
 			title: 'On-Device Training with ONNX Runtime: A deep dive',
 			date: 'July 5th, 2023',
 			blurb:
 				'This blog presents technical details of On-Device training with ONNX Runtime. It explains how On-Device Training works and what are the different steps and artifacts involved in the training process. This information will help you train your models on edge devices.',
 			link: 'https://cloudblogs.microsoft.com/opensource/2023/07/05/on-device-training-with-onnx-runtime-a-deep-dive/',
-			image: 'https://cloudblogs.microsoft.com/opensource/wp-content/uploads/sites/37/2023/06/Open-Source-1.webp'
+			image:
+				'https://cloudblogs.microsoft.com/opensource/wp-content/uploads/sites/37/2023/06/Open-Source-1.webp'
 		},
 		{
 			title:
@@ -45,9 +69,7 @@
 				'Learn how ONNX Runtime accelerates Whisper and makes it easy to deploy on desktop, mobile, in the cloud, and even in the browser.',
 			link: 'https://medium.com/microsoftazure/build-and-deploy-fast-and-portable-speech-recognition-applications-with-onnx-runtime-and-whisper-5bf0969dd56b',
 			image: 'https://miro.medium.com/v2/resize:fit:1100/format:webp/1*DJH8_6GS06-N32tkVhdTOw.png'
-		}
-	];
-	let blogs = [
+		},
 		{
 			title: 'On-Device Training: Efficient training on the edge with ONNX Runtime',
 			date: 'May 31st, 2023',

src/routes/blogs/accelerating-llama-2/+page.svelte

@@ -0,0 +1,264 @@
+<script>
+	import Header from '../../components/header.svelte';
+	import Footer from '../../components/footer.svelte';
+    import figure1 from '../../../images/blogs/accelerating-llama-2/Figure1-LLaMA-2-7B-E2E-Throughput.png'
+    import figure1b from '../../../images/blogs/accelerating-llama-2/Figure1-LLaMA-2-13B-E2E-Throughput.png'
+    import figure2 from '../../../images/blogs/accelerating-llama-2/Figure2-LLaMA-2-7B-Prompt-Latency 1.png'
+    import figure2b from '../../../images/blogs/accelerating-llama-2/Figure2-LLaMA-2-13B-Prompt-Latency.png'
+    import figure3 from '../../../images/blogs/accelerating-llama-2/Figure3-LLaMA-2-7B-Tokens-Generated-Throughput.png'
+    import figure3b from '../../../images/blogs/accelerating-llama-2/Figure3-LLaMA-2-13B-Tokens-Generated-Throughput.png'
+    import figure4 from '../../../images/blogs/accelerating-llama-2/Figure4-LLaMA-2-70B-Model-Throughput.png'
+	import figure5 from '../../../images/blogs/accelerating-llama-2/LLaMA-2OptimizationDiagram-5.png';
+	import figure6 from '../../../images/blogs/accelerating-llama-2/LLaMAWindowsExportRotaryEmbeddingSubgraph-6.jpg';
+	import figure7 from '../../../images/blogs/accelerating-llama-2/RotaryEmbeddingFunctionExample-7.png';
+</script>
+
+<svelte:head>
+	<meta
+		name="description"
+		content="Explore how ONNX Runtime can propel your Llama2 variants for faster inference."
+	/>
+</svelte:head>
+<Header pathvar="" />
+<div class="container mx-auto px-4 md:px-8 lg:px-48 pt-8">
+	<h1 class="text-5xl pb-2">Accelerating LLaMA-2 Inference with ONNX Runtime</h1>
+	<p class="text-neutral">By: <a href="https://www.linkedin.com/in/kunal-v-16315b94" class="text-blue-500">Kunal Vaishnavi</a> and <a href="https://www.linkedin.com/in/parinitaparinita/" class="text-blue-500">Parinita Rahi</a> </p>
+	<p class="text-neutral">14TH NOVEMBER, 2023</p>
+	<div class="py-4">
+		<p class="mb-4">
+			Interested in running Llama2 faster? Let us explore how ONNX Runtime can propel your Llama2
+			variants for faster inference!
+		</p>
+
+		<p class="mb-4">
+			You can now experience significant inference gains—up to 4X faster—for the 7B, 13B, and 70B
+			models, thanks to state-of-the-art fusion and kernel optimizations with ONNX Runtime. This
+			blog details performance enhancements, dives into ONNX Runtime fusion optimizations, multi-GPU
+			inferencing support, and guides you on how to leverage the cross-platform prowess of ONNX
+			Runtime for seamless inferencing across platforms. This is the first in a series of upcoming
+			blogs that will cover additional aspects for efficient memory usage with ONNX Runtime
+			quantization updates, and cross-platform usage scenarios.
+		</p>
+
+		<h2 class="text-blue-500 text-3xl mb-4">Background: Llama2 and Microsoft</h2>
+
+		<p class="mb-4">
+			Llama2 is a state-of-the-art open source LLM from Meta ranging in scale from 7B to 70B
+			parameters (7B, 13B, 70B). Microsoft and Meta <a href="https://blogs.microsoft.com/blog/2023/07/18/microsoft-and-meta-expand-their-ai-partnership-with-llama-2-on-azure-and-windows/" class="text-blue-500">announced</a> their AI on Azure and Windows
+			collaboration in July 2023. As part of the announcement, Llama2 was added to the Azure AI
+			model catalog, which serves as a hub of foundation models that empower developers and machine
+			learning (ML) professionals to easily discover, evaluate, customize, and deploy pre-built
+			large AI models at scale.
+		</p>
+
+		<p class="mb-4">
+			ONNX Runtime allows users to easily integrate the power of this generative AI model into your
+			apps and services with improved optimizations that yield faster inferencing speeds and lower
+			your costs.
+		</p>
+
+		<h2 class="text-blue-500 text-3xl mb-4">
+			Faster Inferencing with New ONNX Runtime Optimizations
+		</h2>
+
+		<p class="mb-4">
+			As part of the new 1.16.2 release, ONNX Runtime now has several built-in optimizations for
+			Llama2, including graph fusions and kernel optimizations. The inference speedups, when
+			compared to Hugging Face (HF) variants of Llama2 in PyTorch compile mode for prompt latency of
+			CUDA FP16, are mentioned below. We see ~3X gains in end-to-end throughput comparisons for both
+			7B and 13B models. The end-to-end throughput or wall-clock throughput shown below is defined
+			as <i>batch size * (prompt length + token generation length) / wall-clock latency</i> where
+			wall-clock latency = the latency from running end-to-end and token generation length = 256
+			generated tokens. The E2E throughput is up to 4.5X more when compared to PyTorch compile.
+		</p>
+        <div class="grid grid-cols-1 lg:grid-cols-2 gap-4">
+            <figure class="px-10 pt-4">
+                <img src={figure1} alt="E2E Throughput Comparisons - Llama-2-7b" />
+            </figure>
+            <figure class="px-10 pt-4 my-auto">
+                <img src={figure1b} alt="E2E Throughput Comparisons - Llama-2-13b" />
+            </figure>
+        </div>
+        <div class="mt-2 mb-4 text-center">
+            Figure 1: E2E Throughput Comparisons
+        </div>
+
+		<h2 class="text-blue-500 text-3xl mb-4">Latency and Throughput</h2>
+
+		<p class="mb-4">
+			The graphs below show latency comparisons between the ONNX Runtime and PyTorch variants of the Llama2
+			7B model on CUDA FP16. Latency here is defined as the time it takes to complete one pass
+			through the model to produce the logits and synchronize the outputs.
+		</p>
+
+        <div class="grid grid-cols-1 lg:grid-cols-2 gap-4">
+            <figure class="px-10 pt-4">
+                <img src={figure2} alt="Prompt Latency Comparisons - Llama-2-7b" />
+            </figure>
+            <figure class="px-10 pt-4 my-auto">
+                <img src={figure2b} alt="Prompt Latency Comparisons - Llama-2-13b" />
+            </figure>
+        </div>
+        <div class="mt-2 mb-4 text-center">
+            Figure 2: Prompt Latency Comparisons
+        </div>
+
+		<p class="mb-4">
+			Token generation throughput below is the average throughput of the first 128 tokens generated.
+			We see up to 3.5X gains in token generation throughput when compared to PyTorch eager and
+			compile modes.
+		</p>
+
+        <div class="grid grid-cols-1 lg:grid-cols-2 gap-4">
+            <figure class="px-10 pt-4">
+                <img src={figure3} alt="Tokens Generated Throughput Comparisons - Llama-2-7b" />
+            </figure>
+            <figure class="px-10 pt-4 my-auto">
+                <img src={figure3b} alt="Tokens Generated Throughput Comparisons - Llama-2-13b" />
+            </figure>
+        </div>
+        <div class="mt-2 mb-4 text-center">
+            Figure 3: Tokens Generated Throughput Comparisons
+        </div>
+
+		<p class="mb-4">More details on these metrics can be found <a href="https://github.com/microsoft/onnxruntime-inference-examples/blob/main/python/models/llama2/README.md" class="text-blue-500">here</a>.</p>
+
+		<h2 class="text-blue-500 text-3xl mb-4">ONNX Runtime with Multi-GPU Inference</h2>
+
+		<p class="mb-4">
+			ONNX Runtime supports multi-GPU inference to enable serving large models. Even in FP16 precision,
+			the LLaMA-2 70B model requires 140GB. Loading the model requires multiple GPUs 
+			for inference, even with a powerful NVIDIA A100 80GB GPU.
+		</p>
+
+		<p class="mb-4">
+			ONNX Runtime applied <a href="https://arxiv.org/pdf/1909.08053.pdf" class="text-blue-500">Megatron-LM</a> Tensor Parallelism on the 70B model to split the
+			original model weight onto different GPUs. Megatron sharding on the 70B model
+			shards the PyTorch model with FP16 precision into 4 partitions, converts each partition into ONNX
+			format, and then applies a new ONNX Runtime graph fusion on the converted ONNX model. The 70B
+			model has ~30 tokens per second throughput for token generation at batch size 1, and
+			end-to-end throughput starts at 30 ms for smaller sequence lengths with these optimizations.
+			You can find additional example scripts <a href="https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/llama/" class="text-blue-500">here</a>.
+		</p>
+
+        <figure class="px-10 pt-4">
+			<img src={figure4} alt="70B Llama2 Model Throughput" class="w-3/5 mx-auto"/>
+			<figcaption class="mt-2 mb-4 text-center">
+				Figure 4: 70B Llama2 Model Throughput
+			</figcaption>
+		</figure>
+
+		<h2 class="text-blue-500 text-3xl mb-4">ONNX Runtime Optimizations</h2>
+		<figure class="px-10 pt-4">
+			<img src={figure5} alt="LLaMA-2 Optimization Diagram" />
+			<figcaption class="mt-2 mb-4 text-center">
+				Figure 5: LLaMA-2 Optimization Diagram
+			</figcaption>
+		</figure>
+
+		<p class="mb-4">
+			The techniques that ONNX Runtime uses for optimizations, such as graph fusions, are applicable
+			to state-of-the-art models. As these models become more complex, the techniques used to apply
+			the graph fusions are adapted to accommodate the extra complexity. For example, instead of
+			manually matching fusion patterns in the graph, ONNX Runtime now supports automated pattern
+			matching. Rather than detect large subgraphs by hand and match the many paths they form,
+			fusion opportunities can instead be identified by exporting a large module as a function and
+			then pattern matching against a function's spec.
+		</p>
+        <figure class="px-10 pt-4">
+			<img src={figure6} alt="Example of Rotary Embedding Function" />
+			<figcaption class="mt-2 mb-4 text-center">
+				Figure 6: Example of Rotary Embedding Function
+			</figcaption>
+		</figure>
+
+		<p class="mb-4">
+			As a concrete example, Figure 6 is an example of the nodes that comprise rotary embedding
+			computations. Pattern matching against this subgraph is cumbersome because of the number of
+			paths to verify. By exporting this as a function, the parent view of the graph will only show
+			the inputs and outputs and represent all these nodes as a single operator.
+		</p>
+
+        <figure class="px-10 pt-4">
+			<img src={figure7} alt="Example of Rotary Embedding Function in Parent Graph" />
+			<figcaption class="mt-2 mb-4 text-center">
+				Figure 7: Example of Rotary Embedding Function in Parent Graph
+			</figcaption>
+		</figure>
+
+		<p class="mb-4">
+			This approach makes it easier to maintain and support future versions of the rotary embedding
+			computations because the pattern matching is only dependent on the operator's inputs and
+			outputs instead of its internal semantic representation. It also allows other existing
+			implementations of rotary embeddings in similar models such as GPT-NeoX, Falcon, Mistral,
+			Zephyr, etc. to be pattern matched and fused with minimal or no changes.
+		</p>
+
+		<p class="mb-4">
+			ONNX Runtime also adds support for the GroupQueryAttention (GQA) operator, which leverages the new
+			Flash Attention V2 algorithm and its optimized kernels to efficiently compute attention. The
+			GQA operator supports past-present buffer sharing between the past key/value cache (past KV
+			cache) and the present key/value cache (present KV cache). By binding the present KV caches to
+			the past KV caches, there is no need to allocate separate on-device memory for both caches.
+			Instead, the past KV caches can be pre-allocated with enough on-device memory so that no new
+			on-device memory needs to be requested during inference. This reduces memory usage when the KV
+			caches become large during compute-intensive workloads and lowers latency by eliminating
+			on-device memory allocation requests. The past-present buffer sharing can be enabled or
+			disabled without needing to change the ONNX model, allowing greater flexibility for end users
+			to decide which approach is best for them.
+		</p>
+
+		<p class="mb-4">
+			In addition to these fusions and kernel optimizations, ONNX Runtime reduces the model’s memory usage. 
+			Besides quantization improvements (which will be covered in a future post), ONNX Runtime compresses the 
+			size of the cosine and sine caches used in each of the rotary embeddings by 50%. The compute kernels in 
+			ONNX Runtime that run the rotary embedding computations can then recognize this format and use their 
+			parallelized implementations to calculate the rotary embeddings more efficiently with less memory usage. 
+			The rotary embedding compute kernels also support interleaved and non-interleaved formats to support both 
+			the <a href="https://github.com/microsoft/Llama-2-Onnx" class="text-blue-500">Microsoft version of LLaMA-2</a>
+			and the Hugging Face version of LLaMA-2 respectively while sharing the same calculations.
+		</p>
+
+		<p class="mb-4">
+			The optimizations work for the <a href="https://huggingface.co/meta-llama" class="text-blue-500">Hugging Face versions</a> (models ending with <i>-hf</i>) and the Microsoft versions. 
+			You can download the optimized HF versions from <a href="https://github.com/microsoft/Llama-2-Onnx/tree/main-CUDA_CPU" class="text-blue-500">Microsoft's LLaMA-2 ONNX repository</a>. Stay tuned for
+			newer Microsoft versions coming soon!
+		</p>
+
+		<h2 class="text-blue-500 text-3xl mb-4">Optimize your own model using Olive</h2>
+
+		<p class="mb-4">
+			Olive is a hardware-aware model optimization tool that incorporates advanced techniques such
+			as model compression, optimization, and compilation. We have made ONNX Runtime optimizations
+			available through Olive so you can streamline the entire optimization process for a given
+			hardware with simple experience.
+		</p>
+
+		<p class="mb-4">
+			Here is an example of <a href="https://github.com/microsoft/Olive/tree/main/examples/llama2" class="text-blue-500">Llama2 optimization with Olive</a>, which harnesses ONNX Runtime
+			optimizations highlighted in this blog. Distinct optimization flows cater to various
+			requirements. For instance, you have the flexibility to choose different data types for
+			quantization in CPU and GPU inference, based on your accuracy tolerance. Additionally, you can
+			fine-tune your own Llama2 model with Olive-QLoRa on client GPUs and perform inference with
+			ONNX Runtime optimizations.
+		</p>
+
+		<h2 class="text-blue-500 text-3xl mb-4">Usage Example</h2>
+
+		<p class="mb-4">
+			Here is a <a href="https://github.com/microsoft/onnxruntime-inference-examples/blob/main/python/models/llama2/LLaMA-2%20E2E%20Notebook.ipynb" class="text-blue-500">sample notebook</a> that shows you an end-to-end example of how you can use the above
+			ONNX Runtime optimizations in your application.
+		</p>
+
+		<h2 class="text-blue-500 text-3xl mb-4">Conclusion</h2>
+
+		<p class="mb-4">
+			The advancements discussed in this blog provide faster Llama2 inferencing with ONNX Runtime,
+			offering exciting possibilities for AI applications and research. With improved performance
+			and efficiency, the horizon is wide open for innovation, and we eagerly await new applications
+			built with Llama2 and ONNX Runtime by its vibrant community of developers. Stay tuned for more
+			updates!
+		</p>
+	</div>
+</div>
+<Footer pathvar="" />

src/routes/blogs/blog-post-featured.svelte

@@ -5,6 +5,7 @@
 	export let date: string;
 	export let link: string;
 	export let image: string;
+	export let imgalt: string;
 	let handleEnter = (e: any) => {
 		anime({
 			targets: e.target,
@@ -31,7 +32,7 @@
 			<div class="card-body">
 				<h2 class="card-title">{title}</h2>
 				<p>{description}</p>
-				<img src={image}/>
+				<img src={image} alt={imgalt}/>
 				<div class="text-right text-blue-500">
 					{date}
 				</div>