/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/** An implementation of the TopK kernel shared between webgl and cpu. */
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import {tensor} from '../ops/tensor_ops';
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import {Tensor} from '../tensor';
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import {NumericDataType, TypedArray} from '../types';
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import {getTypedArrayFromDType} from '../util';
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export function topkImpl<T extends Tensor>(
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x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,
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sorted: boolean): [T, T] {
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// Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
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const lastDim = xShape[xShape.length - 1];
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const [batch, size] = [x.length / lastDim, lastDim];
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const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
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const allTopKIndices = getTypedArrayFromDType('int32', batch * k);
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for (let b = 0; b < batch; b++) {
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const offset = b * size;
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const vals = x.subarray(offset, offset + size);
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const valAndInd: Array<{value: number, index: number}> = [];
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for (let i = 0; i < vals.length; i++) {
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valAndInd.push({value: vals[i], index: i});
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}
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valAndInd.sort((a, b) => b.value - a.value);
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const outOffset = b * k;
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const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
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const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
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for (let i = 0; i < k; i++) {
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topKVals[i] = valAndInd[i].value;
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topKIndices[i] = valAndInd[i].index;
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}
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}
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// Reshape back to the original input shape, except that the last
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// dimension is k.
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const outputShape = xShape.slice();
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outputShape[outputShape.length - 1] = k;
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return [
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tensor(allTopKVals, outputShape, xDtype) as T,
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tensor(allTopKIndices, outputShape, 'int32') as T
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];
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}
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