WPT tests tracker / 2 for remaining ops

[BruceDai]

Here's process of WPT tests for remaining 20 ops with @fdwr‘s great efforts on these operations tolerances, thanks much @fdwr!

Note: Set l2Pool2d being P3 is due to issue #278.

I synced with @fdwr about tolerance definitions for gru/gruCell and lstm/lstmCell，since they're not operators but more like entire miniature graphs themselves, we need community for some help of defining them, thanks.

Operations	WebNN-Baseline implementation	WebNN-Polyfill implementation	WPT tests (PR link)	Priority	Note
hardSwish	√	√	√	P1	It's easy to implement
resample2d	√	√	WIP	P1	Required by Semantic Segmentation - DeepLabv3
pRelu	√	√	√	P1	It's easy to implement
pad	pr	√(updated)	√	P1
elu	√	√	pr	P1	It's easy to implement
instanceNormalization	√	√	WIP	P1	Required by Fast Style Transfer
hardSigmoid	√	√	pr	P1	It's easy to implement
linear	√	√	pr	P1	It's easy to implement
softplus	pr	√	pr	P1	It's easy to implement
softsign	√	√	pr	P1	It's easy to implement
reduceL1	√	√	pr	P2
reduceL2	√	√	pr	P2
reduceLogSum	√	√	pr	P2
reduceLogSumExp	√	√	pr	P2
reduceSumSquare	√	√	pr	P2
gru	√	√	WIP	P2	Required by RNNoise, NSNet
gruCell	√	√	WIP	P2	Required by Recurrent network
lstm	×	×		P2	Required by Recurrent network
lstmCell	×	×		P2	Required by Recurrent network
l2Pool2d	×	√		P3	There's an issue #278

[BruceDai]

Link to #265

[fdwr]

Operator tolerances:

Op	float16	float32	notes
elu	18 ULP	18 ULP	if a >= 0 then a else (expₑ(a) - 1) * alpha
hardSwish	4 ULP	4 ULP	if x <= -threshold then 0 elif x >= threshold else x (x + threshold) / (2 threshold) // threshold = 3
gru	? ULP	? ULP	⏳
gruCell	? ULP	? ULP	⏳
hardSigmoid	2 ULP	2 ULP	clamp(a * alpha + beta, 0, 1)); defaults alpha = 0.2, beta = 0.5
instanceNormalization	? ULP	? ULP	⏳ (a - mean) * scale / sqrt(variance + epsilon) + bias ¹
linear	2 ULP	2 ULP	x * scale + bias
lstm	? ULP	? ULP	⏳
lstmCell	? ULP	? ULP	⏳
l2Pool2d	IEPOE * 2 + 1 ULP	IEPOE * 2 + 1 ULP	sqrt(a0^2 + a1^2 + … + an^2)
pad	0 ULP	0 ULP	should be exact, no math
prelu	1 ULP	1 ULP	if a >= 0 then a else a * slope
reduceL1	<=IEPOE ULP	<=IEPOE ULP	abs(x1) + abs(x2) + ... + abs(xn)
reduceL2	IEPOE * 2 + 1 ULP	IEPOE * 2 + 1 ULP	sqrt(a1^2 + a2^2 + ... + an^2)
reduceLogSum	IEPOE + 18 ULP	IEPOE + 18 ULP	log(a1 + a2 + ... + an)
reduceLogSumExp	IEPOE*2 + 18 ULP	IEPOE*2 + 18 ULP	log(exp(a1) + exp(a2) + ... + exp(an))
reduceSumSquare	IEPOE * 2 ULP	IEPOE * 2 ULP	a1^2 + a2^2 + ... + an^2
resample2d	? ULP	? ULP	⏳
softplus	18 ULP	18 ULP	logₑ(1 + expₑ(a))
softsign	3 ULP	3 ULP	x / (1 + abs(a))

Earlier ones here: https://github.com/webmachinelearning/webnn/issues/265#issuecomment-1256242643

¹ "Instance" normalization is really spatial normalization, where the spatial channels are reduced and normalized, while batch and channel remain independent. So pass a list of axes just beyond the leading batch and channel dimensions (starting at axis 2 up to the last spatial dimension). So for 4D, it's just mean variance normalization with axes: [2,3]. The mean and variance are computed on demand.

[update] Phase 3+:

Op	float16	float32	notes
argMax	0	0
argMin	0	0
cast	1	1
elementwiseIf	0	0
equal	0	0
erf	⏳	⏳
expand	0	0
fillSequence	2	2
gather	0	0
greater	0	0
greaterOrEqual	0	0
identity	0	0
lesser	0	0
lesserOrEqual	0	0
logicalNot	0	0
meanVarianceNormalization	⏳	⏳
reciprocal	2	2
reshape	0	0
sqrt	1	1
triangularMatrix	0	0

[anssiko]

@BruceDai can you provide on update on these remaining tests, can we close this issue?

[BruceDai]

Hi @anssiko, I'm sorry for not updating this status timely.

Please see the above updated table of V1 remaining operations, now except tests for these complex gru/gruCell/lstm/lstmCell operations which are WIP by @fdwr and me , others are almost done (remaining prs were under reviewing or approved).

Regarding to V2 transformers operations, they've been already tracked by https://github.com/webmachinelearning/webnn/issues/485, now there're two remaining prs for constant (fillSequence) and triangular which are discussed on #492 and #494.

Thanks a lot for great supports from @fdwr @huningxin @Honry and @mei1127 ! 👍

[anssiko]

@BruceDai @fdwr much thanks for your work on wpt coverage improvements.

I noted this in #240. We are expected to share an update on the wpt coverage by the upcoming CR publication transition expected next month. 100% test coverage is not expected for this transition, but the closer we get the better.