Diffusion Model Temporal Sparsity for Faster Inference
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Solution Overview
Problem
Diffusion models are inefficient due to their high computational and memory overhead, and existing techniques like quantization and sparsity fail to effectively accelerate them without compromising generation quality.
Innovation Solution
Optimizing diffusion model execution by leveraging a time-dependent activation sparsity pattern, utilizing a heterogeneous processor architecture that distinguishes between sparse and dense computations based on the model's varying activation levels across timesteps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantization and sparsity techniques are applied to diffusion models, then compute and memory efficiency improve, but generation quality deteriorates due to error accumulation and inability to handle varying activation distributions
Solution Approach 1:
The patent applies dynamic sparsity masking that adapts to varying activation distributions across different timesteps and layers of the diffusion model. Instead of using static sparsity patterns, the system dynamically identifies and masks redundant computations based on the actual activation values at each timestep, allowing the sparsity level to change throughout the generation process. This dynamic approach maintains generation quality while achieving compute efficiency improvements.
Solution Approach 2:
The patent implements layer-wise and timestep-specific sparsity masking, where different masking patterns are applied to different layers and timesteps based on their local activation characteristics. Rather than applying a uniform sparsity pattern across the entire model, the system analyzes activation distributions locally and applies appropriate masking strategies to each region, preserving important computations while eliminating redundant ones.
2Ease of manufacture
If uniform quantization scheme is applied across all layers and timesteps, then implementation simplicity improves, but accuracy deteriorates due to varying activation distributions
Solution Approach 1:
The patent employs layer-wise and timestep-specific quantization schemes that adapt to the local activation distributions in different parts of the network and at different timesteps. Each layer and timestep can use its own quantization parameters (bit-width, thresholds, etc.) based on the characteristics of its activations, rather than forcing a uniform quantization scheme across the entire model.
Solution Approach 2:
The system dynamically adjusts quantization parameters such as bit-width and threshold values based on the activation distributions observed at each layer and timestep. This allows the quantization scheme to adapt to changing statistical properties of activations throughout the diffusion process, maintaining precision where needed while reducing precision where acceptable.
3Manufacturing precision
If dense DNN execution is used for diffusion models, then generation quality is maintained, but computational overhead and energy consumption increase significantly
Solution Approach 1:
The patent extracts and eliminates redundant computations from the diffusion model execution by identifying activations and operations that contribute minimally to the final output. Through sparsity masking, the system removes unnecessary computations from the execution graph, executing only the essential operations that preserve generation quality, thereby reducing energy consumption.
Solution Approach 2:
The system applies partial computation by selectively executing only the necessary portion of the model operations at each timestep. Rather than performing all computations uniformly, the sparsity masking enables the system to skip redundant operations while maintaining the essential computational path needed for quality generation.
4Speed
If sparsity masking is applied without considering temporal variations, then computational speed improves, but generation quality deteriorates
Solution Approach 1:
The patent implements temporal-aware sparsity masking that adapts to the dynamic nature of diffusion model activations across timesteps. The masking pattern is not static but changes at each timestep based on the actual activation values, allowing the system to maintain generation quality while achieving speedup by eliminating only the truly redundant computations at each step.
Solution Approach 2:
The system performs preliminary analysis of activation distributions to determine optimal sparsity masking patterns before execution. By pre-computing or pre-identifying which activations are likely to be redundant based on historical or statistical information, the system can apply appropriate masking while preserving quality-critical computations.
Data Source
AI summary
Quantization and sparsity serve as the two pivotal techniques driving dramatic improvements in deep neural network (DNN) performance. However, for diffusion models quantization and sparsity fail to work out-of-the-box due to the unique characteristics of these models. The present disclosure provides for optimization of the execution of a diffusion model based on a time-dependent activation sparsity pattern of the diffusion model, which can provide acceleration of the diffusion model while achieving state-of-the-art generation quality at significantly reduced hardware costs.