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+
+

Accelerating LLaMA-2 Inference with ONNX Runtime

+

14TH NOVEMBER, 2023

+
+

Llama2 with ONNX Runtime: Part 1

+ +

+ Interested in running Llama2 faster? Let us explore how ONNX Runtime can propel your Llama2 + variants for faster inference! +

+ +

+ 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. +

+ +

Background: Llama2 and Microsoft

+ +

+ 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 announced 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. +

+ +

+ 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. +

+ +

+ Faster Inferencing with new ONNX Runtime Optimizations +

+ +

+ 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 batch size * (prompt length + token generation length) / wall-clock latency) 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 faster when compared to PyTorch Compile. +

+
+
+ E2E Throughput Comparisons +
+
+ E2E Throughput Comparisons +
+
+
+ Figure 1: E2E Throughput Comparisons +
+ +

Latency and Throughput

+ +

+ The graphs below show latency comparisons between the ORT 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. +

+ +

Figure 2: Prompt Latency Comparisons

+ +

+ 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. The token generated throughput measured in Tokens per Second (TPS) is 3.5X + faster with ONNX Runtime as compared to PyTorch versions of the model. +

+ +

Figure 3: Tokens Generated Throughput Comparisons

+ +

More details on these metrics can be found here.

+ +

ONNX Runtime with Multi-GPU Inference

+ +

+ ONNX Runtime supports multi-GPU inference to enable serving large models. The LLaMA-2 70B + model has 70 billion parameters, and even in FP16 precision, the model requires 140GB. Loading + the model requires multiple GPUs for inference, even with a powerful NVIDIA A100 80GB GPU. +

+ +

+ ONNX Runtime applied Megatron-LM Tensor Parallelism on meta-llama/Llama-2-70b-hf to split the + original model weight onto different GPUs. Megatron shard on meta-llama/Llama-2-70b-hf model + shards the PyTorch model with FP16 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 here. +

+ +
+ Example of Rotary Embedding Function in Parent Graph +
+ Figure 4: 70B Llama2 Model Throughput +
+
+ +

ONNX Runtime Optimizations

+
+ LLaMA-2 Optimization Diagram +
+ Figure 5: LLaMA-2 Optimization Diagram +
+
+ +

+ 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. +

+
+ Example of Rotary Embedding Function +
+ Figure 6: Example of Rotary Embedding Function +
+
+ +

+ 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. +

+ +
+ Example of Rotary Embedding Function in Parent Graph +
+ Figure 7: Example of Rotary Embedding Function in Parent Graph +
+
+ +

+ This approach makes it easier to maintain and support future versions of the rotary embeddings + 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. +

+ +

+ ONNX Runtime also adds support for 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. +

+ +

+ The optimizations work for the Hugging Face versions (models ending with -hf). You can + download the optimized HF versions from Microsoft's LLaMA-2 ONNX repository. Stay tuned for + newer Microsoft versions coming soon! +

+ +

Optimize your own model using Olive

+ +

+ 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. +

+ +

+ Here is the example of Llama2 optimization with Olive, 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. +

+ +

Usage Example

+ +

+ Here is a sample notebook that shows you an end-to-end example of how you can use the above + ONNX Runtime Optimizations in your application. +

+ +

Conclusion

+ +

+ 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! +

+
+
+

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