gpu_tensor 1.1.4

A comprehensive GPU tensor library for Dart and Flutter, designed to leverage the power of webgpu across platforms for efficient tensor operations.

example/gpu_tensor_example.dart

import 'dart:typed_data';
import 'package:gpu_tensor/gpu_tensor.dart';
import 'package:minigpu/minigpu.dart';

Future<void> main() async {
  // Create a 3x3 tensor (Tensor A) with values 1..9.
  final a = await Tensor.create<Uint8List>([3, 3],
      data: Uint8List.fromList([
        1,
        2,
        3,
        4,
        5,
        6,
        7,
        8,
        9,
      ]),
      dataType: BufferDataType.uint8);

  // Create another 3x3 tensor (Tensor B) with values 9..1.
  final b = await Tensor.create<Uint8List>([3, 3],
      data: Uint8List.fromList([
        9,
        8,
        7,
        6,
        5,
        4,
        3,
        2,
        1,
      ]),
      dataType: BufferDataType.uint8);

  // Elementwise addition.
  final added = await (a + b);
  final addedData = await added.getData();
  print("Elementwise addition result: $addedData");

  // Elementwise subtraction.
  final subtracted = await (a - b);
  final subtractedData = await subtracted.getData();
  print("Elementwise subtraction result: $subtractedData");

  // Scalar multiplication.
  final scalarMult = await a.multiplyScalar(2.0);
  final scalarMultData = await scalarMult.getData();
  print("Scalar multiplication result: $scalarMultData");

  // Matrix multiplication (dot product) of a and b.
  final matMulResult = await a.matMul(b);
  final matMulData = await matMulResult.getData();
  print("Matrix multiplication result: $matMulData");

  // Reshape the matrix multiplication result into a 1-D tensor.
  final reshaped = matMulResult.reshape([9]);
  final reshapedData = await reshaped.getData();
  print("Reshaped matrix multiplication result: $reshapedData");

  // getElement, head, tail
  // Get an element from Tensor A (for a 3x3 matrix, element at indices [1,2] should be 6).
  final elementA = await a.getElement([1, 2]);
  print("Element at [1,2] in Tensor A: $elementA");

  // Use head() helper to get the first 2 rows and 2 columns of Tensor A.
  final headA = await a.head([2, 2]);
  print("Head of Tensor A (first 2 rows, 2 cols): $headA");

  // Use tail() helper to get the last 2 rows and 2 columns of Tensor A.
  final tailA = await a.tail([2, 2], pretty: true);
  print("Tail of Tensor A (last 2 rows, 2 cols): $tailA");

  // FFT demo (1D FFT)
  // Create a real 1D tensor with 8 points.
  const int points1D = 8;
  final Float32List realData1D = Float32List(points1D);
  for (int i = 0; i < points1D; i++) {
    realData1D[i] = i.toDouble();
  }
  final realTensor1D = await Tensor.create([points1D], data: realData1D);
  final fft1dResult = await realTensor1D.fft();
  final fft1dResultData = await fft1dResult.getData();
  print("FFT 1D result: $fft1dResultData");

  // FFT demo (2D FFT)
  // Create a real 2D tensor with 4 rows and 4 columns.
  const int rows = 4;
  const int cols = 4;
  final Float32List realData2D = Float32List(rows * cols);
  for (int i = 0; i < rows * cols; i++) {
    realData2D[i] = i.toDouble();
  }
  final realTensor2D = await Tensor.create([rows, cols], data: realData2D);
  final fft2dResult = await realTensor2D.fft();
  final fft2dResultData = await fft2dResult.getData();
  print("FFT 2D result: $fft2dResultData");
}