Method runWebGLProgram

tfjs-backend-webgl/src/backend_webgl.ts:874–1015

(
      program: GPGPUProgram, inputs: TensorInfo[], outputDtype: DataType,
      customUniformValues?: number[][], preventEagerUnpackingOfOutput = false,
      customTexShape?: [number, number])

  }

  runWebGLProgram(
      program: GPGPUProgram, inputs: TensorInfo[], outputDtype: DataType,
      customUniformValues?: number[][], preventEagerUnpackingOfOutput = false,
      customTexShape?: [number, number]): TensorInfo {
    const output = this.makeTensorInfo(program.outputShape, outputDtype);
    const outData = this.texData.get(output.dataId);
    if (program.packedOutput) {
      outData.isPacked = true;
    }
    if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {
      const texelShape = customTexShape != null ?
          customTexShape :
          tex_util.getDenseTexShape(program.outputShape);
      // For a densely packed output, we explicitly set texShape
      // so it doesn't get assigned later according to our typical packing
      // scheme wherein a single texel can only contain values from adjacent
      // rows/cols.
      outData.texShape = texelShape.map(d => d * 2) as [number, number];
    }
    if (program.outTexUsage != null) {
      outData.usage = program.outTexUsage;
    }

    if (util.sizeFromShape(output.shape) === 0) {
      // Short-circuit the computation since the result is empty (has 0 in its
      // shape).
      outData.values =
          util.getTypedArrayFromDType(output.dtype as 'float32', 0);
      return output;
    }

    const dataToDispose: TensorInfo[] = [];
    const inputsData: TensorData[] = inputs.map(input => {
      if (input.dtype === 'complex64') {
        throw new Error(
            `GPGPUProgram does not support complex64 input. For complex64 ` +
            `dtypes, please separate the program into real and imaginary ` +
            `parts.`);
      }

      let texData = this.texData.get(input.dataId);

      if (texData.texture == null) {
        if (!program.packedInputs &&
            util.sizeFromShape(input.shape) <=
                env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {
          // Upload small tensors that live on the CPU as uniforms, not as
          // textures. Do this only when the environment supports 32bit floats
          // due to problems when comparing 16bit floats with 32bit floats.
          // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it
          // possible for packed shaders to sample from uniforms.
          return {
            shape: input.shape,
            texData: null,
            isUniform: true,
            uniformValues: texData.values as TypedArray
          };
        }