tasq/node_modules/onnxruntime-web/lib/onnxjs/backends/webgl/ops/matmul-pack.ts

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

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

import {getActivationSnippet, InternalActivationAttributes} from './fuse-utils';
import {getBiasForMatmul} from './matmul';

const createPackedMatmulProgramMetadata = (hasBias: boolean, cacheHint: string) => ({
  name: 'MatMul (packed)',
  inputNames: hasBias ? ['A', 'B', 'Bias'] : ['A', 'B'],
  inputTypes: hasBias ? [TextureType.packed, TextureType.packed, TextureType.packed] :
                        [TextureType.packed, TextureType.packed],
  cacheHint
});

const createPackedMatmulProgramInfo =
    (inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[],
     activationAttributes: InternalActivationAttributes): ProgramInfo => {
      const hasBias = inputs.length > 2;
      const processBias = hasBias ? 'value += getBiasForMatmul();' : '';
      const aShape = inputs[0].dims;
      const bShape = inputs[1].dims;
      const outputShape = BroadcastUtil.calcShape(aShape, bShape, true);
      const isBroadcast = !ShapeUtil.areEqual(inputs[0].dims, inputs[1].dims);

      if (!outputShape) {
        throw new Error('Can\'t use matmul on the given tensors');
      }
      const sharedDim = aShape[aShape.length - 1];
      const sharedDimIndex = Math.ceil(sharedDim / 2);
      const aRank = aShape.length;
      const bRank = bShape.length;

      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
      const coordsDataType = getCoordsDataType(outputShape.length);
      const outRank = outputShape.length;
      const allGlChannels = getGlChannels();
      const {activationFunction, applyActivation} = getActivationSnippet(activationAttributes);

      const getBiasForMatmulSnippet =
          hasBias ? `${getBiasForMatmul(coordsDataType, allGlChannels, inputs[2].dims, outputShape, true)}` : '';

      const getBcastedSamplerForMatmulSnippet =
          isBroadcast ? `${getBcastSamplerForMatmul(coordsDataType, allGlChannels, inputs, outputShape)}` : '';

      const getSamplerAInLoopSnippet = isBroadcast ? 'getAAtOutCoordsMatmul(i)' : `getA(${getA(allGlChannels, aRank)})`;
      const getSamplerBInLoopSnippet = isBroadcast ? 'getBAtOutCoordsMatmul(i)' : `getB(${getB(allGlChannels, bRank)})`;
      const getOutputCoordsSnippet = isBroadcast ? '' : `${coordsDataType} rc =
          getOutputCoords(); int lastDim = rc.${allGlChannels[outRank - 1]}; rc.${allGlChannels[outRank - 1]} =
          rc.${allGlChannels[outRank - 2]}; rc.${allGlChannels[outRank - 2]} = lastDim;
      `;
      const shaderSource = `
            ${getBcastedSamplerForMatmulSnippet}
            ${getBiasForMatmulSnippet}
            ${activationFunction}
            void main() {
              ${getOutputCoordsSnippet}

              vec4 value = vec4(0);
              for (int i = 0; i < ${sharedDimIndex}; i++) {
                vec4 a = ${getSamplerAInLoopSnippet};
                vec4 b = ${getSamplerBInLoopSnippet};

                value += (a.rrbb * b.rgrg);
                value += (a.ggaa * b.baba);
              }
              ${processBias}
              ${applyActivation}
              ${glsl.output} = value;
            }`;
      return {
        ...metadata,
        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.packed},
        shaderSource,
        hasMain: true
      };
    };

export const createPackedMatmulProgramInfoLoader =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[],
     activationAttributes: InternalActivationAttributes): ProgramInfoLoader => {
      const metadata = createPackedMatmulProgramMetadata(inputs.length > 2, activationAttributes.activationCacheKey);
      return {
        ...metadata,
        get: () => createPackedMatmulProgramInfo(inferenceHandler, metadata, inputs, activationAttributes)
      };
    };

function getBcastSamplerForMatmul(
    coordsDataType: string, allGlChannels: readonly string[], inputs: Tensor[], outShape: readonly number[]): string {
  let unpackedACoordsSnippet = [];
  let unpackedBCoordsSnippet = [];

  const inAShape = inputs[0].dims;
  const inBShape = inputs[1].dims;

  const inARank = inAShape.length;
  const inBRank = inBShape.length;

  const outRank = outShape.length;
  const rankADiff = outRank - inARank;
  const rankBDiff = outRank - inBRank;

  unpackedACoordsSnippet = inAShape.map((s, i) => `coords.${allGlChannels[i + rankADiff]}`);
  unpackedACoordsSnippet[inARank - 1] = 'i*2';
  unpackedACoordsSnippet.join(', ');
  unpackedBCoordsSnippet = inBShape.map((s, i) => `coords.${allGlChannels[i + rankBDiff]}`);
  unpackedBCoordsSnippet[inBRank - 2] = 'i*2';
  unpackedBCoordsSnippet.join(', ');

  const broadcastADims = BroadcastUtil.getBroadcastDims(inAShape, outShape);
  const broadcastBDims = BroadcastUtil.getBroadcastDims(inBShape, outShape);

  const coordsASnippet = broadcastADims.map(d => `coords.${allGlChannels[d + rankADiff]} = 0;`).join('\n');
  const coordsBSnippet = broadcastBDims.map(d => `coords.${allGlChannels[d + rankBDiff]} = 0;`).join('\n');
  const swapDimSnippet = `int lastDim = coords.${allGlChannels[outRank - 1]};
  coords.${allGlChannels[outRank - 1]} = coords.${allGlChannels[outRank - 2]};
  coords.${allGlChannels[outRank - 2]} = lastDim;`;

  const getBcastSamplerMatmulSource = `
vec4 getAAtOutCoordsMatmul(int i) {
  ${coordsDataType} coords = getOutputCoords();
  ${swapDimSnippet}
  ${coordsASnippet}
  vec4 outputValue = getA(${unpackedACoordsSnippet});
  return outputValue;
}

vec4 getBAtOutCoordsMatmul(int i) {
  ${coordsDataType} coords = getOutputCoords();
  ${swapDimSnippet}
  ${coordsBSnippet}
  vec4 outputValue = getB(${unpackedBCoordsSnippet});
  return outputValue;
}`;

  return getBcastSamplerMatmulSource;
}

function getA(allGlChannels: string[], rank: number): string {
  let res = '';
  for (let i = 0; i < rank - 2; i++) {
    res += `rc.${allGlChannels[i]}, `;
  }
  res += `rc.${allGlChannels[rank - 2]}, ` +
      'i*2';
  return res;
}

function getB(allGlChannels: string[], rank: number): string {
  let res = '';
  for (let i = 0; i < rank - 2; i++) {
    res += `rc.${allGlChannels[i]}, `;
  }
  res += 'i*2, ' +
      `rc.${allGlChannels[rank - 1]}`;
  return res;
}