Function sparseToDense

tfjs-backend-webgpu/src/kernels/SparseToDense.ts:28–130

(args: {
  inputs: SparseToDenseInputs,
  backend: WebGPUBackend,
  attrs: SparseToDenseAttrs
})

import {tile} from './Tile';

export function sparseToDense(args: {
  inputs: SparseToDenseInputs,
  backend: WebGPUBackend,
  attrs: SparseToDenseAttrs
}): TensorInfo {
  const {inputs, backend, attrs} = args;
  const {sparseIndices, sparseValues, defaultValue} = inputs;
  const {outputShape} = attrs;

  const {sliceRank, numUpdates, sliceSize, strides, outputSize} =
      backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);

  const sumDupeIndices = false;
  if (sparseValues.dtype === 'string') {
    const indicesBuf = backend.bufferSync<Rank, 'int32'>(sparseIndices);
    const updatesBuf = backend.bufferSync<Rank, 'string'>(sparseValues);
    const $defaultValue = util.decodeString(
        backend.readSync(defaultValue.dataId)[0] as Uint8Array);
    const outBuf = scatterImplCPU(
        indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates,
        sliceRank, strides, $defaultValue, sumDupeIndices);
    return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
  }

  const flattenShape = [outputSize / sliceSize, sliceSize];

  const $sparseIndices = reshape({
    inputs: {x: sparseIndices},
    backend,
    attrs: {shape: [numUpdates, sliceRank]}
  });
  const $sparseValues = sparseValues.shape.length ?
      reshape({
        inputs: {x: sparseValues},
        backend,
        attrs: {shape: [numUpdates, sliceSize]}
      }) :
      identity({inputs: {x: sparseValues}, backend});

  const type = $sparseValues.dtype;
  const zero =
      backend.makeTensorInfo([], type, util.makeZerosTypedArray(1, type));

  // Fill output tensor with the default value.
  const $defaultValue = reshape({
    inputs: {x: defaultValue},
    backend,
    attrs: {shape: Array(flattenShape.length).fill(1)}
  });
  const $denseValues =
      tile({inputs: {x: $defaultValue}, backend, attrs: {reps: flattenShape}});

  const size = util.sizeFromShape([numUpdates, sliceSize]);
  const uniformData = [
    {type: 'int32', data: [sliceRank]},
    {type: 'int32', data: strides},
    {type: 'int32', data: [size]},
  ];