Implement more reduction#44
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reduceL1 / reduceL2 reduceLogSum / reduceLogSumExp reduceSumSquare fixes webmachinelearning#17
| const outputShape = input.shape.slice(); | ||
| for (let i = 0; i < axesLen; ++i) { | ||
| const axis = axes[i] >= 0 ? axes[i] : axes[i] + rank; | ||
| const axis = axes[i]; |
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Aah, nice, we can simplify the testing and implementations after Ningxin's removal of the negative axes policy.
| * @return {Tensor} | ||
| */ | ||
| export function reduceSumSquare(input, options = {}) { | ||
| return reduceSum(pow(input, new Scalar(2)), options); |
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Formulas all look correct, but wasn't there a GitHub issue with ReduceL2 ambiguity?
The formula I recall was ReduceL2 = sqrt(a1^2 + a2^2 + ... + an^2).
(btw, I always have to re-lookup the formulas because I keep thinking "reduceSumSquare" means x.sum().square(), but actually it means x.square().sum() - so square and then reduce sum)
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but wasn't there a GitHub issue with ReduceL2 ambiguity?
Are there other explanations for ReduceL2 besides ReduceL2 = sqrt(a1^2 + a2^2 + ... + an^2) ?
Reference: https://en.wikipedia.org/wiki/Norm_(mathematics)#p-norm
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Oh never mind, it was actually L2 pooling, not reduction: webmachinelearning/webnn#278
| let output = new Tensor(input.shape); | ||
| const shape = new Array(input.rank).fill(1); | ||
| shape[axis] = -1; | ||
| shape[axis] = null; |
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🤔 Ningxin's PR removed the special handling of negative numbers related to axes, but we still have this special case with axes here with reshape. I wonder if this too should be a policy resolved by higher level frameworks first, and they just pass the resolved shape before it reaches the WebNN API (or does keeping Reshape's special null axis handling make the composition of other WebNN ops simpler, like your instanceNormalization implementation which utilizes reshape?) *this comment is not blocking either way
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to simplify the implementation
Sorry, I don't catch your point, could you please explain how to simplify the implementation?
And I submitted a pr webmachinelearning/webnn#367 to Spec, please also have a review, thanks.
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This PR is based on #37.
@fdwr @huningxin PTAL at the third commit, thanks.