onnxruntime-web/lib/wasm/jsep/webgpu/ops/gemm.ts

A Javascript library for running ONNX models on browsers

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

import {DataType} from '../../../wasm-common';
import {TensorView} from '../../tensor-view';
import {GemmUtil, ShapeUtil} from '../../util';
import {AttributeWithCacheKey} from '../attribute-with-cache-key';
import {ComputeContext, ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform} from '../types';

import {createTensorShapeVariables, IndicesHelper, inputVariable, outputVariable, ShaderHelper, UniformsArrayType} from './common';

const validateInputs = (inputs: readonly TensorView[]): void => {
  if (!inputs) {
    throw new Error('Input is missing');
  }
  if (inputs.length < 2 || inputs.length > 3) {
    throw new Error('Invaid input number.');
  }

  // 'C' can be of dimensionality 0, 1 or 2 only
  if (inputs.length === 3 && inputs[2].dims.length > 2) {
    throw new Error('Invalid input shape of C');
  }

  if ((inputs[0].dataType !== inputs[1].dataType) ||
      (inputs.length === 3 && inputs[0].dataType !== inputs[2].dataType)) {
    throw new Error('Input types are mismatched');
  }
};

export interface GemmAttributes extends AttributeWithCacheKey {
  transA: boolean;
  transB: boolean;
  alpha: number;
  beta: number;
}

const createGemmProgramInfo = (inputs: readonly TensorView[], attributes: GemmAttributes): ProgramInfo => {
  const aShape = inputs[0].dims.slice();
  const bShape = inputs[1].dims.slice();
  const [M, N, K] = GemmUtil.getShapeOfGemmResult(
      aShape, attributes.transA, bShape, attributes.transB, inputs.length === 3 ? inputs[2].dims : undefined);
  const outputShape = [M, N];
  if (!outputShape) {
    throw new Error('Can\'t use gemm on the given tensors');
  }
  const outputSize = ShapeUtil.size(outputShape);
  const programUniforms: ProgramUniform[] = [
    {type: DataType.uint32, data: outputSize}, {type: DataType.uint32, data: M}, {type: DataType.uint32, data: N},
    {type: DataType.uint32, data: K}, {type: DataType.float, data: attributes.alpha},
    {type: DataType.float, data: attributes.beta}
  ];
  const inputDependencies: ProgramInputTensorInfoDependency[] = ['type', 'type'];
  if (inputs.length === 3) {
    programUniforms.push(...createTensorShapeVariables(inputs[2].dims));
    inputDependencies.push('rank');
  }
  programUniforms.push(...createTensorShapeVariables(outputShape));

  const getShaderSource = (shaderHelper: ShaderHelper) => {
    let line = '';
    if (attributes.transA && attributes.transB) {
      line = 'value += a[k * uniforms.M + m] * b[n * uniforms.K + k];';
    } else if (attributes.transA && !attributes.transB) {
      line = 'value += a[k * uniforms.M + m] * b[k * uniforms.N + n];';
    } else if (!attributes.transA && attributes.transB) {
      line = 'value += a[m * uniforms.K + k] * b[n * uniforms.K + k];';
    } else if (!attributes.transA && !attributes.transB) {
      line = 'value += a[m * uniforms.K + k] * b[k * uniforms.N + n];';
    }

    const calculateAlpha = attributes.alpha === 1 ? '' : 'value *= uniforms.alpha;';
    const a = inputVariable('a', inputs[0].dataType, inputs[0].dims);
    const b = inputVariable('b', inputs[1].dataType, inputs[1].dims);
    const dataType = a.type.value;
    let c: IndicesHelper|null = null;
    const variables = [a, b];
    if (inputs.length === 3) {
      c = inputVariable('c', inputs[2].dataType, inputs[2].dims.length);
      variables.push(c);
    }
    const output = outputVariable('output', inputs[0].dataType, outputShape.length);
    variables.push(output);
    const uniforms: UniformsArrayType = [
      {name: 'output_size', type: 'u32'}, {name: 'M', type: 'u32'}, {name: 'N', type: 'u32'}, {name: 'K', type: 'u32'},
      {name: 'alpha', type: 'f32'}, {name: 'beta', type: 'f32'}
    ];
    return `
  ${shaderHelper.registerUniforms(uniforms).declareVariables(...variables)}

  ${shaderHelper.mainStart()}
    ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes('uniforms.output_size')}

    let m = global_idx / uniforms.N;
    let n = global_idx % uniforms.N;

    var value = ${dataType}(0);
    for (var k: u32 = 0u; k < uniforms.K; k++) {
      ${line}
    }

    ${calculateAlpha}
    ${(() => {
      if (c != null) {
        return `let cOffset = ${c.broadcastedIndicesToOffset('vec2(m, n)', output)}; value += ${
            dataType}(uniforms.beta) * ${c.getByOffset('cOffset')};`;
      }
      return '';
    })()}
    output[global_idx] = value;
  }`;
  };

  return {
    name: 'Gemm',
    shaderCache: {hint: `${attributes.cacheKey}`, inputDependencies},
    getRunData: () => ({
      outputs: [{dims: outputShape, dataType: inputs[0].dataType}],
      dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)},
      programUniforms
    }),
    getShaderSource,
  };
};

export const parseGemmAttributes = (attributes: Record<string, unknown>): GemmAttributes => {
  const transA = attributes.transA as boolean;
  const transB = attributes.transB as boolean;
  const alpha = attributes.alpha as number;
  const beta = attributes.beta as number;
  return {transA, transB, alpha, beta, cacheKey: `${attributes.transA};${attributes.transB};${attributes.alpha === 1}`};
};

export const gemm = (context: ComputeContext, attributes: GemmAttributes): void => {
  validateInputs(context.inputs);
  context.compute(createGemmProgramInfo(context.inputs, attributes));
};