onnxruntime/js/web/lib/onnxjs/backends/webgl/ops/batch-normalization.ts

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

import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';
import {Graph} from '../../../graph';
import {OperatorImplementation, OperatorInitialization} from '../../../operators';
import {Tensor} from '../../../tensor';
import {getGlsl} from '../glsl-source';
import {WebGLInferenceHandler} from '../inference-handler';
import {ProgramInfo, TextureType} from '../types';

export interface BatchNormalizationAttributes extends AttributeWithCacheKey {
  epsilon: number;
  momentum: number;
  spatial: number;
}

const batchNormalizationProgramMetadata = {
  name: 'BatchNormalization',
  inputNames: ['A', 'Scale', 'B', 'Mean', 'Variance'],
  inputTypes:
      [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked, TextureType.unpacked, TextureType.unpacked]
};

export const batchNormalization: OperatorImplementation<BatchNormalizationAttributes> =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: BatchNormalizationAttributes): Tensor[] => {
      validateInputs(inputs);
      const output = inferenceHandler.run(
          {
            ...batchNormalizationProgramMetadata,
            cacheHint: attributes.cacheKey,
            get: () => createBatchNormalizationProgramInfo(inferenceHandler, inputs, attributes)
          },
          inputs);
      return [output];
    };

export const parseBatchNormalizationAttributes: OperatorInitialization<BatchNormalizationAttributes> =
    (node: Graph.Node): BatchNormalizationAttributes => {
      const epsilon = node.attributes.getFloat('epsilon', 1e-5);
      const momentum = node.attributes.getFloat('momentum', 0.9);
      const spatial = node.attributes.getInt('spatial', 1);
      return createAttributeWithCacheKey({epsilon, momentum, spatial});
    };

const createBatchNormalizationProgramInfo =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: BatchNormalizationAttributes):
        ProgramInfo => {
          const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
          const rank = inputs[0].dims.length;
          const [scaleWidth, scaleHeight] =
              inferenceHandler.calculateTextureWidthAndHeight(inputs[1].dims, TextureType.unpacked);
          const shaderSource = `
  float process(int[${rank}] indices) {
    vec2 position = offsetToCoords(indices[1], ${scaleWidth}, ${scaleHeight});
    float scale = getColorAsFloat(${glsl.texture2D}(Scale, position));
    float mean = getColorAsFloat(${glsl.texture2D}(Mean, position));
    float variance = getColorAsFloat(${glsl.texture2D}(Variance, position));
    float b = getColorAsFloat(${glsl.texture2D}(B, position));

    return scale * ( (_A(indices) - mean) / sqrt(variance + float(${attributes.epsilon})) ) + b;
  }`;
          return {
            ...batchNormalizationProgramMetadata,
            output: {dims: inputs[0].dims, type: inputs[0].type, textureType: TextureType.unpacked},
            shaderSource
          };
        };

const validateInputs = (inputs: Tensor[]): void => {
  if (!inputs || inputs.length !== 5) {
    throw new Error('BatchNormalization requires 5 inputs.');
  }

  const X = inputs[0];
  const scale = inputs[1];
  const B = inputs[2];
  const mean = inputs[3];
  const var_ = inputs[4];

  // input should atleast have three dimensions - N,C,dim1,...,dimn
  // other inputs can have only one dimensions
  if (X.dims.length < 3 || scale.dims.length !== 1 || B.dims.length !== 1 || mean.dims.length !== 1 ||
      var_.dims.length !== 1) {
    throw new Error('invalid input shape.');
  }
  if (scale.dims[0] !== X.dims[1] || B.dims[0] !== X.dims[1] || mean.dims[0] !== X.dims[1] ||
      var_.dims[0] !== X.dims[1]) {
    throw new Error('invalid input shape.');
  }
  if ((X.type !== 'float32' && X.type !== 'float64') || (scale.type !== 'float32' && scale.type !== 'float64') ||
      (B.type !== 'float32' && B.type !== 'float64') || (mean.type !== 'float32' && mean.type !== 'float64') ||
      (var_.type !== 'float32' && var_.type !== 'float64')) {
    throw new Error('invalid input tensor types.');
  }
};