MLTensor

The MLTensor interface represents a tensor that can be used as input or output to an MLGraph.

MLTensorDescriptor

Before diving into MLTensor, let’s understand the MLTensorDescriptor dictionary that describes its characteristics.

Web IDL

dictionary MLTensorDescriptor : MLOperandDescriptor {
  boolean readable = false;
  boolean writable = false;
};

Properties

• readable (boolean)

  • Determines if the tensor’s contents can be read using readTensor(tensor) or readTensor(tensor, outputData)
  • Default value: false

• writable (boolean)

  • Determines if the tensor’s contents can be written using writeTensor()
  • Default value: false

MLTensor Interface

Web IDL

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLTensor {
  readonly attribute MLOperandDataType dataType;
  readonly attribute FrozenArray<unsigned long> shape;
  readonly attribute boolean readable;
  readonly attribute boolean writable;
  undefined destroy();
}

Properties

• dataType (readonly)

  • Returns the tensor’s data type as specified in its descriptor
  • Type: MLOperandDataType

• shape (readonly)

  • Returns the tensor’s dimensions as specified in its descriptor
  • Type: FrozenArray<unsigned long>

• readable (readonly)

  • Returns whether the tensor’s contents can be read
  • Type: boolean

• writable (readonly)

  • Returns whether the tensor’s contents can be written
  • Type: boolean

Methods

destroy()

Releases the resources associated with the MLTensor.

Syntax

tensor.destroy()

Return value

• undefined

Description

The destroy() method is idempotent, meaning multiple calls to this method on the same tensor will have the same effect as a single call.

Memory Allocation

The memory backing an MLTensor is allocated according to implementation-defined requirements of the associated MLContext and MLTensorDescriptor. These requirements may include:

• Specific byte alignment constraints
• Allocation in particular memory pools
• Other implementation-specific requirements

Examples

Creating and Using an MLTensor

 async function mlTensor() {
  // Define tensor descriptor
  const tensorDescriptor = {
    dataType: "float32",
    shape: [1, 28, 28, 1],
    readable: true,
    writable: true
  };
 
  try {
    // Create tensor through MLContext and MLTensorDescriptor
    const context = await navigator.ml.createContext({
      deviceType: 'gpu'
    });
    const tensor = await context.createTensor(tensorDescriptor);
 
    // Use the tensor
    console.log("Tensor shape:", tensor.shape);
    console.log("Data type:", tensor.dataType);
    
    // Clean up when done
    tensor.destroy();
  } catch (error) {
    console.error("Error creating tensor:", error);
  }
}

Reading MLTensor Data

async function readTensorData(tensor) {
  if (tensor.readable) {
    try {
      const data = await context.readTensor(tensor);
      console.log("Tensor data:", data);
    } catch (error) {
      console.error("Error reading tensor:", error);
    }
  }
}

Specifications

Security Requirements

• This API requires a secure context (HTTPS or localhost)
• Available in:
  • Window context
  • Dedicated Web Workers

Browser Compatibility

See Browser Compatibility: WebNN API