Function immutableKernelsMixedWithoutFloats

test/issues/267-immutable-sub-kernels.js:169–221

(mode)

describe('issue #267 sub kernels mixed');
function immutableKernelsMixedWithoutFloats(mode) {
  function value1(value) {
    return value + 10;
  }

  function value2(value) {
    return value + 50;
  }

  const gpu = new GPU({ mode });
  const kernel = gpu.createKernelMap(
    {
      valueOutput1: value1,
      valueOutput2: value2,
    },
    function (a, b) {
      value1(a[this.thread.x]);
      return value2(b[this.thread.x]) + 100;
    },
    {
      output: [1],
      immutable: true,
      pipeline: true,
      precision: 'unsigned',
    }
  );

  // start with a value on CPU
  const output1 = kernel([10], [20]);

  // reuse that output, simulating that this value will be monitored, and updated via the same kernel
  // this is often used in neural networks
  const output2 = kernel(output1.result, output1.valueOutput2);
  const output3 = kernel(output2.result, output2.valueOutput2);

  function toArray(value) {
    return value.toArray ? value.toArray() : value;
  }

  assert.equal(toArray(output1.valueOutput1)[0], 20); // 10 + 10
  assert.equal(toArray(output1.valueOutput2)[0], 70); // 20 + 50
  assert.equal(toArray(output1.result)[0], 170); // (20 + 50) + 100

  assert.equal(toArray(output2.valueOutput1)[0], 180); // 170 + 10
  assert.equal(toArray(output2.valueOutput2)[0], 120); // 70 + 50
  assert.equal(toArray(output2.result)[0], 220); // (70 + 50) + 100

  assert.equal(toArray(output3.valueOutput1)[0], 230); // 220 + 10
  assert.equal(toArray(output3.valueOutput2)[0], 170); // 120 + 50
  assert.equal(toArray(output3.result)[0], 270); // (120 + 50) + 100

  gpu.destroy();
}

(GPU.isKernelMapSupported ? test : skip)('Issue #267 immutable kernel & sub-kernel output without floats - auto', () => {
  immutableKernelsMixedWithoutFloats();