A Practical Guide to Memory Optimization for MNN Audio Models
Audio models often face the problem of high memory usage for long sequences, which can be optimized by combining strategies:
- stream processing technology: 1) Encapsulate the framing logic using 'MNN::Express::Module' 2) Incrementalize the input via 'Tensor::createFromHostPtr' 3) Set the 'Interpreter::SessionMode' to 'SESSION_MEMORY_BUFFER'
- Model Cutting Program: Use 'MNN::Express::Variable::split' to slice long sequences into 10s-long slices, together with 'SequenceModule' to realize slice processing.
- memory reuse technique: 1) Configure 'BackendConfig::memory' to 'MemoryMode::MEMORY_REUSE' 2) Call ' Interpreter::releaseModelBuffer' to release the intermediate result in time.
- Quantitative compression program: 8-bit quantization of features such as the Meier spectrum, implemented in conjunction with the 'MNN::Compression::quantizeModel' tool
Typical configuration parameters:
Interpreter::SessionConfig sessionConfig;.
sessionConfig.backendConfig.memory = MemoryMode::MEMORY_REUSE;
sessionConfig.backendConfig.precision = Precision_Low;
This answer comes from the articleMNN-LLM-Android: MNN Multimodal Language Model for Android ApplicationsThe
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