ONNX Support

Vollo also provides a tool for compiling ML models defined in ONNX.

vollo-onnx is a command line tool which allows the user to specify an input ONNX file and produces a .vollo program as output. The user specifies a path to the input .onnx file:

Arguments:
  <INPUT>
          Path to the input .onnx file

The user can specify:

• The output path:

  -o, --output <OUTPUT>
        Output path for the compiled program file
  
        [default: program.vollo]
  

• A name for the model:

      --model-name <MODEL_NAME>
        Name of the model
  

• The hardware configuration to use based on a JSON file (this JSON file can be generated using the Config method save in the vollo_compiler python module):

      --hw-config <HW_CONFIG_JSON>
        Path to the hardware config JSON file
  

• A name for the hardware configuration to use (from a set of preset configs).

      --hw-config-preset <PRESET_NAME>
        Hardware configuration to use, chosen from a set of presets
  
        [possible values: ia420f-c6b32, ia840f-c3b64]
  

• Which transformations to perform on the model. Currently the only available transformation is the streaming transform Example 2: CNN:

      --streaming-transform <STREAMING_AXIS>
        Axis on which to perform the streaming transform in the NNIR graph
  
        If unspecified, no streaming transform is performed
  

• The input shape of the model. This is required if the ONNX model has dynamic input shapes. Vollo requires that the shape of the input be known at compile-time:

      --override-input-shape <SHAPE>
        If the model has dynamic input shapes, the user must pass a fixed input shape
  
        Example: 10,100,250
  

• Whether to elide all compute logic and generate a program with only IO logic. This is useful for determining IO latencies.

      --io-only
        Generate a program with IO only - useful for testing IO latencies
  

• Whether to use the VM to decide whether certain optimizations should be applied. Note: this is experimental and likely to change.

      --optimize-transforms
          Runs the VM to decide whether to apply certain transformations or not
  

Simplifying ONNX Models

vollo-onnx has a limited list of supported ONNX nodes. Often ONNX models can be over-complicated, and contain unnecessary shaping operations. It is recommended that onnx-simplifier be used before calling vollo-onnx on an ONNX model to remove these unnecessary shaping operations which aren't supported by vollo-onnx:

onnx-sim <model.onnx> <model-sim.onnx> --overwrite-input-shape <model-input-shape>

It is also recommended to use the --overwrite-input-shape with onnx-simplifier, as this can enable further simplifications and better constant folding.

Using ONNX from Python

ONNX models can also be imported and translated to NNIR models directly in python using the static NNIR method from_onnx. This also requires that the input shape be specified if the ONNX model has dynamic input shapes, otherwise it can be None.

onnx_nnir = vollo_compiler.NNIR.from_onnx(onnx_path, input_shape)

Supported Nodes

Tensors are expected to be in float32 format, unless they are used as indices / axes (in which case they should be int64s).

vollo-onnx supports models with the following nodes: