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lrn_gpu.ts
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/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import {GPGPUProgram} from './gpgpu_math';
export class LRNProgram implements GPGPUProgram {
variableNames = ['x'];
outputShape: number[] = [];
userCode: string;
constructor(
xShape: number[], radius: number, bias: number, alpha: number,
beta: number) {
const rad = radius;
const maxD = xShape[3] - 1;
this.outputShape = xShape;
// optimize pow(bias + alpha * sum, -beta)
// src: https://github.com/tensorflow/tensorflow/..
// blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
// tensorflow/core/kernels/mkl_lrn_op.cc#L320
let powOperator;
const basis = `float(${bias}) + float(${alpha}) * sum`;
if (beta === 0.5) {
powOperator = `inversesqrt(${basis})`;
} else if (beta === 1.0) {
powOperator = `1.0/(${basis})`;
} else {
powOperator = `exp(log(${basis}) * float(-${beta}));`;
}
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${rad}; j <= ${rad}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${maxD}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${powOperator};
setOutput(val);
}
`;
}
}