onnxruntime/js/web/lib/onnxjs/backends/webgl/ops/matmul.ts

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

import {MatMul} from '../../../ops/matmul';
import {Tensor} from '../../../tensor';
import {BroadcastUtil} from '../../../util';
import {WebGLInferenceHandler} from '../inference-handler';
import {ProgramInfo, RunData, WebGLOperator} from '../types';
import {WebGLMatMulPacked} from './matmul-pack';

export class WebGLMatMul extends MatMul implements WebGLOperator {
  packedImpl: WebGLMatMulPacked;
  unpackedImpl: WebGLUnpackedMatMul;
  constructor() {
    super();
    this.packedImpl = new WebGLMatMulPacked();
    this.unpackedImpl = new WebGLUnpackedMatMul();
  }

  run(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] {
    if (inferenceHandler.session.pack) {
      return inferenceHandler.run(this.packedImpl, inputs);
    } else {
      return inferenceHandler.run(this.unpackedImpl, inputs);
    }
  }

  createProgramInfo(handler: WebGLInferenceHandler, inputs: Tensor[]): ProgramInfo {
    if (handler.session.pack && inputs[0].dims.length > 1) {
      return this.packedImpl.createProgramInfo(handler, inputs);
    } else {
      return this.unpackedImpl.createProgramInfo(handler, inputs);
    }
  }

  createRunData(handler: WebGLInferenceHandler, programInfo: ProgramInfo, inputs: Tensor[]): RunData {
    if (handler.session.pack && inputs[0].dims.length > 1) {
      return this.packedImpl.createRunData(handler, programInfo, inputs);
    } else {
      return this.unpackedImpl.createRunData(handler, programInfo, inputs);
    }
  }
}

export class WebGLUnpackedMatMul extends MatMul implements WebGLOperator {
  run(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] {
    return inferenceHandler.run(this, inputs);
  }
  createProgramInfo(handler: WebGLInferenceHandler, inputs: Tensor[]): ProgramInfo {
    const aShape = inputs[0].dims;
    const bShape = inputs[1].dims;
    const outputShape = BroadcastUtil.calcShape(aShape, bShape, true);
    if (!outputShape) {
      throw new Error('Can\'t use matmul on the given tensors');
    }
    const rank = outputShape.length;
    const arank = aShape.length;
    const brank = bShape.length;
    const sharedDim = aShape[aShape.length - 1];
    const shaderSource = `
      float process(int indices[${rank}]) {
          int a[${arank}];
          int b[${brank}];
          bcastMatmulIndices_A(indices, a);
          bcastMatmulIndices_B(indices, b);

          float value;
          for (int k=0; k<${sharedDim}; ++k) {
              a[${arank - 1}] = k;
              b[${brank - 2}] = k;
              value += _A(a) * _B(b);
          }
          return value;
      }`;
    return {
      inputLayouts: inputs.map(t => handler.getOrCreateTextureLayout(t)),
      outputLayout: handler.createTextureLayoutFromShape(outputShape),
      samplers: ['A', 'B'],
      shaderSource,
    };
  }
  createRunData(handler: WebGLInferenceHandler, programInfo: ProgramInfo, inputs: Tensor[]): RunData {
    const inputTDs = inputs.map((t, i) => handler.getOrCreateTextureData(t, programInfo.inputLayouts[i]));
    return {
      inputTextureDatas: inputTDs,
      outputTextureData: handler.createTextureDataFromLayout(programInfo.outputLayout, inputTDs[0].tensor.type),
      uniformData: {}
    };
  }
}