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/**
* AUTO-GENERATED - DO NOT EDIT. Source: https://github.com/gpuweb/cts
**/import { anyOf } from '../../../../../util/compare.js';import { toVector } from '../../../../../util/conversion.js';
import { FP } from '../../../../../util/floating_point.js';
import { cartesianProduct } from '../../../../../util/math.js';
import { selectNCases } from '../../case.js';
import { makeCaseCache } from '../../case_cache.js';
// Using a bespoke implementation of make*Case and generate*Cases here
// since faceForwardIntervals is the only builtin with the API signature
// (vec, vec, vec) -> vec
//
// Additionally faceForward has significant complexities around it due to the
// fact that `dot` is calculated in its operation, but the result of dot isn't
// used to calculate the builtin's result.
/**
* @returns a Case for `faceForward`
* @param argumentKind what kind of floating point numbers being operated on
* @param parameterKind what kind of floating point operation should be performed,
* should be the same as argumentKind, except for abstract
* @param x the `x` param for the case
* @param y the `y` param for the case
* @param z the `z` param for the case
* @param check what interval checking to apply
* */
function makeCase(
argumentKind,
parameterKind,
x,
y,
z,
check)
{
const fp = FP[argumentKind];
x = x.map(fp.quantize);
y = y.map(fp.quantize);
z = z.map(fp.quantize);
const results = FP[parameterKind].faceForwardIntervals(x, y, z);
if (check === 'finite' && results.some((r) => r === undefined)) {
return undefined;
}
// Stripping the undefined results, since undefined is used to signal that an OOB
// could occur within the calculation that isn't reflected in the result
// intervals.
const define_results = results.filter((r) => r !== undefined);
return {
input: [
toVector(x, fp.scalarBuilder),
toVector(y, fp.scalarBuilder),
toVector(z, fp.scalarBuilder)],
expected: anyOf(...define_results)
};
}
/**
* @returns an array of Cases for `faceForward`
* @param argumentKind what kind of floating point numbers being operated on
* @param parameterKind what kind of floating point operation should be performed,
* should be the same as argumentKind, except for abstract
* @param xs array of inputs to try for the `x` param
* @param ys array of inputs to try for the `y` param
* @param zs array of inputs to try for the `z` param
* @param check what interval checking to apply
*/
function generateCases(
argumentKind,
parameterKind,
xs,
ys,
zs,
check)
{
// Cannot use `cartesianProduct` here due to heterogeneous param types
return cartesianProduct(xs, ys, zs).
map((e) => makeCase(argumentKind, parameterKind, e[0], e[1], e[2], check)).
filter((c) => c !== undefined);
}
// Cases: [f32|f16|abstract]_vecN_[non_]const
const cases = ['f32', 'f16', 'abstract'].
flatMap((trait) =>
[2, 3, 4].flatMap((dim) =>
[true, false].map((nonConst) => ({
[`${trait}_vec${dim}_${nonConst ? 'non_const' : 'const'}`]: () => {
if (trait === 'abstract' && nonConst) {
return [];
}
if (trait !== 'abstract') {
return generateCases(
trait,
trait,
FP[trait].sparseVectorRange(dim),
FP[trait].sparseVectorRange(dim),
FP[trait].sparseVectorRange(dim),
nonConst ? 'unfiltered' : 'finite'
);
} else {
// Restricting the number of cases, because a vector of abstract floats needs to be returned, which is costly.
return selectNCases(
'faceForward',
20,
generateCases(
trait,
// faceForward has an inherited accuracy, so is only expected to be as accurate as f32
'f32',
FP[trait].sparseVectorRange(dim),
FP[trait].sparseVectorRange(dim),
FP[trait].sparseVectorRange(dim),
nonConst ? 'unfiltered' : 'finite'
)
);
}
}
}))
)
).
reduce((a, b) => ({ ...a, ...b }), {});
export const d = makeCaseCache('faceForward', cases);
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