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Use hash based filtering instead of stochastic
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zoddicus committed Mar 6, 2024
1 parent c362232 commit c8800ee
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Showing 9 changed files with 179 additions and 198 deletions.
204 changes: 102 additions & 102 deletions src/resources/cache/hashes.json
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@@ -1,110 +1,110 @@
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}
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Original file line number Diff line number Diff line change
@@ -1,16 +1,16 @@
import { FP } from '../../../../util/floating_point.js';
import { sparseMatrixF64Range } from '../../../../util/math.js';
import { StochasticFilter } from '../../../../util/stochastic_filter.js';
import { selectNCases } from '../case.js';
import { makeCaseCache } from '../case_cache.js';

const sf = new StochasticFilter(0);
// Cases: matKxR_matCxK
const mat_mat_cases = ([2, 3, 4] as const)
.flatMap(k =>
([2, 3, 4] as const).flatMap(cols =>
([2, 3, 4] as const).map(rows => ({
[`mat${k}x${rows}_mat${cols}x${k}`]: () => {
return sf.filter(
return selectNCases(
50,
FP.abstract.generateMatrixPairToMatrixCases(
sparseMatrixF64Range(k, rows),
sparseMatrixF64Range(cols, k),
Expand Down
Original file line number Diff line number Diff line change
@@ -1,17 +1,21 @@
import { FP } from '../../../../util/floating_point.js';
import { sparseMatrixF64Range, sparseScalarF64Range } from '../../../../util/math.js';
import { selectNCases } from '../case.js';
import { makeCaseCache } from '../case_cache.js';

// Cases: matCxR_scalar
const mat_scalar_cases = ([2, 3, 4] as const)
.flatMap(cols =>
([2, 3, 4] as const).map(rows => ({
[`mat${cols}x${rows}_scalar`]: () => {
return FP.abstract.generateMatrixScalarToMatrixCases(
sparseMatrixF64Range(cols, rows),
sparseScalarF64Range(),
'finite',
FP.abstract.multiplicationMatrixScalarInterval
return selectNCases(
50,
FP.abstract.generateMatrixScalarToMatrixCases(
sparseMatrixF64Range(cols, rows),
sparseScalarF64Range(),
'finite',
FP.abstract.multiplicationMatrixScalarInterval
)
);
},
}))
Expand All @@ -23,11 +27,14 @@ const scalar_mat_cases = ([2, 3, 4] as const)
.flatMap(cols =>
([2, 3, 4] as const).map(rows => ({
[`scalar_mat${cols}x${rows}`]: () => {
return FP.abstract.generateScalarMatrixToMatrixCases(
sparseScalarF64Range(),
sparseMatrixF64Range(cols, rows),
'finite',
FP.abstract.multiplicationScalarMatrixInterval
return selectNCases(
50,
FP.abstract.generateScalarMatrixToMatrixCases(
sparseScalarF64Range(),
sparseMatrixF64Range(cols, rows),
'finite',
FP.abstract.multiplicationScalarMatrixInterval
)
);
},
}))
Expand Down
Original file line number Diff line number Diff line change
@@ -1,17 +1,21 @@
import { FP } from '../../../../util/floating_point.js';
import { sparseMatrixF64Range, sparseVectorF64Range } from '../../../../util/math.js';
import { selectNCases } from '../case.js';
import { makeCaseCache } from '../case_cache.js';

// Cases: matCxR_vecC
const mat_vec_cases = ([2, 3, 4] as const)
.flatMap(cols =>
([2, 3, 4] as const).map(rows => ({
[`mat${cols}x${rows}_vec${cols}`]: () => {
return FP.abstract.generateMatrixVectorToVectorCases(
sparseMatrixF64Range(cols, rows),
sparseVectorF64Range(cols),
'finite',
FP.abstract.multiplicationMatrixVectorInterval
return selectNCases(
50,
FP.abstract.generateMatrixVectorToVectorCases(
sparseMatrixF64Range(cols, rows),
sparseVectorF64Range(cols),
'finite',
FP.abstract.multiplicationMatrixVectorInterval
)
);
},
}))
Expand All @@ -23,11 +27,14 @@ const vec_mat_cases = ([2, 3, 4] as const)
.flatMap(rows =>
([2, 3, 4] as const).map(cols => ({
[`vec${rows}_mat${cols}x${rows}`]: () => {
return FP.abstract.generateVectorMatrixToVectorCases(
sparseVectorF64Range(rows),
sparseMatrixF64Range(cols, rows),
'finite',
FP.abstract.multiplicationVectorMatrixInterval
return selectNCases(
50,
FP.abstract.generateVectorMatrixToVectorCases(
sparseVectorF64Range(rows),
sparseMatrixF64Range(cols, rows),
'finite',
FP.abstract.multiplicationVectorMatrixInterval
)
);
},
}))
Expand Down
43 changes: 40 additions & 3 deletions src/webgpu/shader/execution/expression/case.ts
Original file line number Diff line number Diff line change
@@ -1,11 +1,13 @@
import { crc32 } from '../../../../common/util/crc32.js';
import { ROArrayArray } from '../../../../common/util/types.js';
import { ScalarBuilder, Value, Vector, i32, u32, abstractInt } from '../../../util/conversion.js';
import { assert } from '../../../../common/util/util.js';
import { abstractInt, i32, ScalarBuilder, u32, Value, Vector } from '../../../util/conversion.js';
import {
QuantizeFunc,
cartesianProduct,
QuantizeFunc,
quantizeToI32,
quantizeToU32,
quantizeToI64,
quantizeToU32,
} from '../../../util/math.js';

import { Expectation } from './expectation.js';
Expand All @@ -25,6 +27,41 @@ export type Case = {
/** CaseList is a list of Cases */
export type CaseList = Array<Case>;

/**
* Filters a given set of Cases down to a target number of cases by
* randomly selecting which Cases to return.
*
* The selection algorithm is deterministic and stable for a case's
* inputs.
*
* This means that if a specific case is selected is not affected by the
* presence of other cases in the list, so in theory it is possible to create a
* pathological set of cases such that all or not of the cases are selected
* in spite of the target number.
*
* This is a trade-off from guaranteeing stability of the selected cases over
* small changes, so the target number of cases is more of a suggestion. It is
* still guaranteed that if you set n0 < n1, then the invocation with n0 will
* return at most the number of cases that n1 does, it just isn't guaranteed to
* be less.
*
* @param n number of cases targeted be returned. Expected to be a positive
* integer. If equal or greater than the number of cases, then all the
* cases are returned. 0 is not allowed, since it is likely a
* programming error, because if the caller intentionally wants 0
* items, they can just use [].
* @param cases list of Cases to be selected from.
*/
export function selectNCases(n: number, cases: CaseList): CaseList {
assert(n > 0 && Math.round(n) === n, `n ${n} is expected to be a positive integer`);
const count = cases.length;
if (n >= count) {
return cases;
}

return cases.filter(c => n * (0xffff_ffff / count) > crc32(c.input.toString()));
}

/**
* A function that performs a binary operation on x and y, and returns the
* expected result.
Expand Down
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