AI BriefingOptimizing PyTorch with Fused MLP
Key point
Using PyTorch Profiler to analyze the overhead occurring in MLP structures and explaining an optimization method through Fused MLP.
Details
By analyzing the behavior of nn.Linear layers using PyTorch Profiler, you can understand the relationship between CPU scheduling overhead and GPU kernel execution. A simple combination of matrix multiplication (matmul) and addition (add) is executed as separate kernels, causing unnecessary overhead.
When constructing an MLP(Multilayer Perceptron) block, as multiple linear layers and activation functions are repeated, the number of kernel executions increases, which leads to performance degradation. To solve this, the Fused MLP technique, which combines multiple operations into a single kernel, is needed.
This guide covers the following key processes:
- Internal behavior of nn.Linear and profiling analysis
- The impact of the Transpose operation occurring during matrix multiplication
- The principle of reducing operation overhead through Kernel Fusion
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