Depthwise Parameter Reordering for Lower-Memory LLM Inference
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Solution Overview
Problem
Existing neural network models, particularly Large Language Models (LLMs), face significant computational, memory, and power inefficiencies due to the high resource demands of Multi-Layer Perceptron (MLP) blocks, which are not adequately addressed by current optimization techniques that overlook tensor activation patterns and hardware architectural nuances.
Innovation Solution
Implementing Iterative Multi-Layer Perceptron Block with Parameter Splits (IMBPS) through depthwise parameter reordering and buffer sharing strategies, optimizing memory layout and computational efficiency by reducing activation sizes and packing overheads, and aligning with CPU server architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MLP block implementations are used in LLMs, then model accuracy and comprehensive task performance are maintained, but computational resource requirements and memory usage increase significantly
Solution Approach 1:
The MLP block parameters are segmented into multiple groups (e.g., W1a, W1b, W2a, W2b) that can be processed independently and in parallel. This segmentation allows the computational workload to be distributed across multiple processing units or time steps, reducing the peak memory bandwidth requirements and enabling more efficient utilization of available computational resources while maintaining the same overall transformation function.
Solution Approach 2:
The patent introduces dynamic iterative processing where the MLP block is applied multiple times with different parameter groups in sequence. This dynamic approach allows the system to achieve the same or better accuracy through repeated smaller transformations rather than a single large transformation, enabling better resource management and reduced peak memory usage during each iteration.
2Measurement precision
If advanced neural network models with more parameters are deployed, then task accuracy and capability improve, but memory usage and power consumption increase
Solution Approach 1:
By segmenting the weight matrices into multiple smaller parameter groups, the patent reduces the memory footprint required to store all parameters simultaneously. Each iterative step only requires loading a subset of parameters into memory, thereby reducing peak memory usage while maintaining the full model capacity across multiple iterations.
Solution Approach 2:
The patent applies the MLP transformation partially across multiple iterations rather than executing the full transformation in a single step. Each iteration applies a partial transformation using a subset of parameters, and the cumulative effect of multiple partial applications achieves the same or better accuracy as a single full application, while using less memory at any given time.
3Device complexity
If standard MLP block processing is used, then implementation simplicity is maintained, but computational efficiency and inference speed decrease
Solution Approach 1:
The patent introduces dynamic iterative processing where the MLP block is applied multiple times with different parameter groups. This dynamic approach enables better utilization of parallel processing capabilities and memory hierarchies, improving computational efficiency through multiple smaller computations that can be optimized for speed rather than a single large computation.
Solution Approach 2:
The patent performs preliminary organization of parameters into distinct groups (W1a, W1b, W2a, W2b) that are pre-configured for iterative processing. This preliminary structuring enables more efficient memory access patterns and parallel computation during inference, improving computational efficiency without significantly increasing implementation complexity.
Data Source
AI summary
Techniques for processing input data in a neural network are disclosed. A sequence of neural network operations of at least one layer of the neural network is decomposed. Following decomposition, the sequence of neural network operations is reordered to form a reordered sequence of operations for the at least one layer. The input data for the at least one layer is then processed via the reordered sequence of operations.