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Machine Learning Tools in Cmajor

Cmajor makes a great platform for high-performance execution (inference) of real-time machine learning tasks. ML inference involves a lot of matrix operators, and the Cmajor JIT engine can do a great job of vectorising that kind of code. In most cases it equals or outperforms equivalent C++ code by taking advantage of processor-specific SIMD extensions where available, and is real-time safe by design.

Two commonly used ML engines for audio tasks are ONNX and RTNeural, and we provide tools to convert these models directly into pure Cmajor code.

Other ML frameworks such as Pytorch or Tensorflow provide conversion tools to/from ONNX, so ONNX can be used as an intermediate format when converting other models to Cmajor.


ONNX

The ONNX Intermediate Representation (IR) is a common format used for exchanging ML models. An ONNX IR file includes not only the model’s architecture but also its weights and other metadata.

ONNX Operators

ONNX operators provide a standardised way to represent these operations, regardless of the deep learning framework used to define or train the model. This means that if you have a neural network model built in one framework (e.g. PyTorch or TensorFlow) and you want to run it on a different platform or framework, you can use ONNX to convert and execute the model seamlessly with Cmajor.

Using the ONNX to Cmajor Python script

You can run cmajor/tools/onnx/onnxToCmajor.py to do the conversion. Command-line options are:

    --model <file>       Specifies the ONNX model file to convert
    --patchDir <folder>  Specifies a folder into which the generated patch will be written

  If no patchDir argument is supplied, the output will be a single Cmajor file.

To run this script, options are given as:

onnxToCmajor.py --model "path/to/your/model.onnx" --patchDir "path/to/output/patch"

This will generate the following files in the given directory:

  • model.cmajor - Contains all the nodes, connections and weights to describe the model

  • onnx.cmajor - Contains definitions of the ONNX operators required by the model

  • model.cmajorpatch - As with all cmajor manifest files, this describes the patch’s properties and contains links to the other files in the patch

Currently supported ONNX Operators:

The ONNX format contains a huge number of operators. Cmajor supports a subset of the more commonly used ones, which will grow over time:

Tanh, Reshape, MatMul, Gemm, Add, Unsqueeze, Squeeze, Conv, GRU, Pad, Transpose, Constant, LSTM, Slice, Gather, Concat


RTNeural

RTNeural is a C++ ML framework aimed at more real-time tasks.

You can run cmajor/tools/rtneural/rtneuralToCmajor.py to perform the conversion, with arguments:

    --model <file>       Specifies the RTNeural model file to convert
    --patchDir <folder>  Specifies a folder into which the generated patch will be written
    --name <name>        Optional name for the model - defaults to "Model"
    --useFloat64         By default we use float32, but this changes the generated model to float64

  If no patchDir argument is supplied, the output will be a single Cmajor file.

e.g.

rtneuralToCmajor.py --model "path/to/your/model.json" --patchDir "path/to/output/patch"

Supported Operators

We currently support the following RTNeural features:

  • Layers: Dense, conv1d, conv2d, GRU, PReLU, BatchNorm1D, BatchNorm2D, LSTM

  • Activations: tanh, ReLU, Sigmoid, Softmax, ELu, PReLU


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