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import { abstractInt, bool, f16, i32, u32 } from '../../../../util/conversion.js';
import { FP, FPInterval } from '../../../../util/floating_point.js';
import { fullI32Range, fullI64Range, fullU32Range } from '../../../../util/math.js';
import { makeCaseCache } from '../case_cache.js';
const f16FiniteRangeInterval = new FPInterval(
'f16',
FP.f16.constants().negative.min,
FP.f16.constants().positive.max
);
// Cases: f32_matCxR_[non_]const
// Note that f32 values may be not exactly representable in f16 and/or out of range.
const f32_mat_cases = ([2, 3, 4] as const)
.flatMap(cols =>
([2, 3, 4] as const).flatMap(rows =>
([true, false] as const).map(nonConst => ({
[`f32_mat${cols}x${rows}_${nonConst ? 'non_const' : 'const'}`]: () => {
return FP.f32.generateMatrixToMatrixCases(
FP.f32.sparseMatrixRange(cols, rows),
nonConst ? 'unfiltered' : 'finite',
FP.f16.correctlyRoundedMatrix
);
},
}))
)
)
.reduce((a, b) => ({ ...a, ...b }), {});
// Cases: f16_matCxR_[non_]const
const f16_mat_cases = ([2, 3, 4] as const)
.flatMap(cols =>
([2, 3, 4] as const).flatMap(rows =>
([true, false] as const).map(nonConst => ({
[`f16_mat${cols}x${rows}_${nonConst ? 'non_const' : 'const'}`]: () => {
// Input matrix is of f16 types, use f16.generateMatrixToMatrixCases.
return FP.f16.generateMatrixToMatrixCases(
FP.f16.sparseMatrixRange(cols, rows),
nonConst ? 'unfiltered' : 'finite',
FP.f16.correctlyRoundedMatrix
);
},
}))
)
)
.reduce((a, b) => ({ ...a, ...b }), {});
// Cases: abstract_float_matCxR
// Note that abstract float values may be not exactly representable in f16
// and/or out of range.
const abstract_float_mat_cases = ([2, 3, 4] as const)
.flatMap(cols =>
([2, 3, 4] as const).map(rows => ({
[`abstract_float_mat${cols}x${rows}`]: () => {
return FP.abstract.generateMatrixToMatrixCases(
FP.abstract.sparseMatrixRange(cols, rows),
'finite',
FP.f16.correctlyRoundedMatrix
);
},
}))
)
.reduce((a, b) => ({ ...a, ...b }), {});
export const d = makeCaseCache('unary/f16_conversion', {
bool: () => {
return [
{ input: bool(true), expected: f16(1.0) },
{ input: bool(false), expected: f16(0.0) },
];
},
u32_non_const: () => {
return [...fullU32Range(), 65504].map(u => {
return { input: u32(u), expected: FP.f16.correctlyRoundedInterval(u) };
});
},
u32_const: () => {
return [...fullU32Range(), 65504]
.filter(v => f16FiniteRangeInterval.contains(v))
.map(u => {
return { input: u32(u), expected: FP.f16.correctlyRoundedInterval(u) };
});
},
i32_non_const: () => {
return [...fullI32Range(), 65504, -65504].map(i => {
return { input: i32(i), expected: FP.f16.correctlyRoundedInterval(i) };
});
},
i32_const: () => {
return [...fullI32Range(), 65504, -65504]
.filter(v => f16FiniteRangeInterval.contains(v))
.map(i => {
return { input: i32(i), expected: FP.f16.correctlyRoundedInterval(i) };
});
},
abstract_int: () => {
return [...fullI64Range(), 65504n, -65504n]
.filter(v => f16FiniteRangeInterval.contains(Number(v)))
.map(i => {
return { input: abstractInt(i), expected: FP.f16.correctlyRoundedInterval(Number(i)) };
});
},
// Note that f32 values may be not exactly representable in f16 and/or out of range.
f32_non_const: () => {
return FP.f32.generateScalarToIntervalCases(
[...FP.f32.scalarRange(), 65535.996, -65535.996],
'unfiltered',
FP.f16.correctlyRoundedInterval
);
},
f32_const: () => {
return FP.f32.generateScalarToIntervalCases(
[...FP.f32.scalarRange(), 65535.996, -65535.996],
'finite',
FP.f16.correctlyRoundedInterval
);
},
// Note that abstract float values may be not exactly representable in f16.
abstract_float: () => {
return FP.abstract.generateScalarToIntervalCases(
[...FP.abstract.scalarRange(), 65535.996, -65535.996],
'finite',
FP.f16.correctlyRoundedInterval
);
},
// All f16 values are exactly representable in f16.
f16: () => {
return FP.f16.scalarRange().map(f => {
return { input: f16(f), expected: FP.f16.correctlyRoundedInterval(f) };
});
},
...f32_mat_cases,
...f16_mat_cases,
...abstract_float_mat_cases,
});
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