Model Loader API

1. Introduction

For the introduction and use cases, please see the explainer.md.

For illustration purposes, the API and examples use the TF Lite flatbuffer format.

2. API

enum MLModelFormat {
  // Tensorflow-lite flatbuffer.
  "tflite" 
};
enum MLDevicePreference {
  // Let the backend selects the most suitable device.
  "auto",
  // The backend will use GPU to do model inference. If some operator is not
  // supported by GPU, it will fall back to CPU.
  "gpu",
  // The backend will use CPU to do model inference.
  "cpu"
};
enum MLPowerPreference {
  // Let the backend selects the most suitable behavior.
  "auto",
  // Prioritizes execution speed over power consumption.
  "high-performance",
  // Prioritizes power consumption over other considerations such as execution
  // speed.
  "low-power",
};
dictionary MLContextOptions {
  // Preferred kind of device to use.
  MLDevicePreference devicePreference = "auto";
  // Preference as related to power consumption.
  MLPowerPreference powerPreference = "auto";
  // Model format for the model loader API.
  MLModelFormat modelFormat = "tflite";
  
  // Number of thread to use.
  // "0" means the backend can determine it automatically.
  unsigned long numThreads = 0;
};
[Exposed=Window]
interface ML {
  Promise<MLContext> createContext(optional MLContextOptions options = {});
};
enum MLDataType {
  // "Unknown" doesn’t mean "unsupported". The background can support more types
  // than which are explicitly listed here (e.g., TfLite has complex numbers).
  // We treat them as "unknown" to avoid exposing too many details of the
  // backends from the beginning.
  "unknown",
  "int64",
  "uint64",
  "float64",
  "int32",
  "uint32",
  "float32",
  "int16",
  "uint16",
  "float16",
  "int8",
  "uint8",
  "bool",
};
dictionary MLTensor {
  required ArrayBufferView data;
  required sequence<unsigned long> dimensions;
};
dictionary MLTensorInfo {
  required DOMString name;
  required MLDataType type;
  required sequence<unsigned long> dimensions;
};
[SecureContext, Exposed=Window]
interface MLModel {
  Promise<record<DOMString, MLTensor>> compute(record<DOMString, MLTensor> inputs);      
  sequence<MLTensorInfo> inputs();
  sequence<MLTensorInfo> outputs();
};
[Exposed=Window]
interface MLModelLoader {
  constructor(MLContext context);
  Promise<MLModel> load(ArrayBuffer modelBuffer);
};

3. Examples

// First, create an MLContext. This is consistent with the WebNN API. And we will 
// add two new fields, “numThread” and "modelFormat". 
const context = await navigator.ml.createContext(
                                     { devicePreference: "cpu",
                                       powerPreference: "low-power",
                                       numThread: 0,   // the default 0 means 
                                                       // "decide automatically". 
                                       modelFormat: "tflite" });
// Then create the model loader using the ML context.
loader = new MLModelLoader(context);
// In the first version, we only support loading models from ArrayBuffers. We 
// believe this covers most of the usage cases. Web developers can download the 
// model, e.g., by the fetch API. We can add new "load" functions in the future
// if they are really needed.
const modelUrl = 'https://path/to/model/file';
const modelBuffer = await fetch(modelUrl)
                            .then(response => response.arrayBuffer());
// Load the model.
model = await loader.load(modelBuffer);
// Use the `model.compute` function to get the output of the model from some 
// inputs.  Example ways of using this function includes, 
// 1. When there is only one input tensor of the model, one can simply input the 
// tensor, without specifying the name of it (the user can still designate this 
// input tensor by name if they like).
z = await model.compute({ data: new Float32Array([10]), 
                          dimensions: [1]) });
// 2. When there are multiple input tensors, the user has to designate the name 
// of the input tensors by their names.
z = await model.compute({ x: { data: new Float32Array([10]), 
                               dimensions: [1] },
                          y: { data: new Float32Array([20]), 
                               dimensions: [1] } });
// 3. The client can also specify the output tensor. This is consistent with the 
// WebNN API and can be useful, e.g., when the output tensor is a GPU buffer. At 
// this time, the function will return an empty promise. The dimension of the 
// output tensor specified must match the dimensions of the output tensor of the 
// model. 
z_buffer = ml.tensor({data: new Float64Array(1), 
                      dimensions: [1] });
await model.compute({ data: new Float32Array([10]), 
                      dimensions: [1] },
                    z_buffer);
// For the output tensor(s),
// Similar to the input arguments, if there is only one output tensor, the 
// `compute` function returns a tensor in case 1 and 2, and there is no need to 
// specify the name of the output tensor in case 3. But if there are multiple 
// output tensors, the output in case 1 and 2 will be a map from tensor name to 
// tensors, and in case 3, the output argument must be a map from tensor name to
// tensors too.
// For case 1 and 2, where the actual output data locate will depend on the 
// context: if it is CPU context, the output tensor’s buffer will be RAM buffer(s)
// and if the context is GPU context, the output tensor’s buffer will be GPU 
// buffer(s).