onnxruntime-web/lib/onnxjs/backends/webgl/ops/dot-product.ts

A Javascript library for running ONNX models on browsers

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import {Tensor} from '../../../tensor';
import {ShapeUtil} from '../../../util';
import {getGlsl} from '../glsl-source';
import {WebGLInferenceHandler} from '../inference-handler';
import {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';

import {getActivationSnippet, InternalActivationAttributes} from './fuse-utils';
import {calculateIm2ColDims} from './im2col';

const createDotProductProgramMetadata = (hasBias: boolean, attributes: InternalActivationAttributes) => ({
  name: 'ConvDotProduct',
  inputNames: hasBias ? ['Im2Col', 'K', 'B'] : ['Im2Col', 'K'],
  inputTypes: hasBias ? [TextureType.unpacked, TextureType.packedLastDimension, TextureType.unpacked] :
                        [TextureType.unpacked, TextureType.packedLastDimension],
  cacheKey: attributes.activationCacheKey
});

const createDotProductProgramInfo =
    (inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: readonly Tensor[],
     outputShape: number[], attributes: InternalActivationAttributes): ProgramInfo => {
      const xshape = inputs[0].dims;
      const kshape = inputs[1].dims;
      const adjustedKernelShape = [kshape[0], Math.ceil((xshape[1] * kshape[2] * kshape[3]) / 4)];
      const im2colShape = calculateIm2ColDims(xshape, kshape, outputShape);
      const [kWidth, kHeight] =
          inferenceHandler.calculateTextureWidthAndHeight(adjustedKernelShape, TextureType.packedLastDimension);

      const im2colStrides = ShapeUtil.computeStrides(im2colShape);
      const [im2colWidth, im2colHeight] =
          inferenceHandler.calculateTextureWidthAndHeight(im2colShape, TextureType.packedLastDimension);
      const rank = outputShape.length;

      const initValue = (inputs.length < 3) ? '0.0' : '_B(b)';
      const sharedDim = Math.ceil(xshape[1] * kshape[2] * kshape[3] / 4);
      const {activationFunction, applyActivation} = getActivationSnippet(attributes);
      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
      const shaderSource = `
${activationFunction}
float process(int indices[${rank}]) {
  int b[1];
  b[0] = indices[1];
  int im2col[4];
  im2col[0] = indices[0];
  im2col[1] = indices[2];
  im2col[2] = indices[3];
  int im2colOffset = im2col[0] * ${im2colStrides[0]} + im2col[1] * ${im2colStrides[1]} + im2col[2] * ${
          im2colStrides[2]};
  int kernelOffset = indices[1] * ${adjustedKernelShape[1]};
  float value = ${initValue};
  for (int i = 0; i < ${sharedDim}; ++i) {
    vec2 im2colCoords = offsetToCoords(im2colOffset, ${im2colWidth}, ${im2colHeight});
    vec2 kernelCoords = offsetToCoords(kernelOffset, ${kWidth}, ${kHeight});
    value += dot(${glsl.texture2D}(Im2Col, im2colCoords), ${glsl.texture2D}(K, kernelCoords));
    ++im2colOffset;
    ++kernelOffset;
  }
  ${applyActivation}
  return value;
}`;
      return {
        ...metadata,
        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},
        shaderSource
      };
    };

export const createDotProductProgramInfoLoader =
    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], outputShape: number[],
     attributes: InternalActivationAttributes): ProgramInfoLoader => {
      const metadata = createDotProductProgramMetadata(inputs.length > 2, attributes);
      return {
        ...metadata,
        get: () => createDotProductProgramInfo(inferenceHandler, metadata, inputs, outputShape, attributes)
      };
    };