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Cumsum.ts
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Cumsum.ts
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/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import {backend_util, Cumsum, CumsumAttrs, CumsumInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, upcastType, util} from '@tensorflow/tfjs-core';
import {MathBackendCPU} from '../backend_cpu';
import {assertNotComplex} from '../cpu_util';
import {transpose} from './Transpose';
export function cumsum(
args: {inputs: CumsumInputs, backend: MathBackendCPU, attrs: CumsumAttrs}):
TensorInfo {
const {inputs, backend, attrs} = args;
const {x} = inputs;
const {axis, exclusive, reverse} = attrs;
assertNotComplex(x, 'cumsum');
const permutation = backend_util.getAxesPermutation([axis], x.shape.length);
let $x = x;
if (permutation != null) {
$x = transpose({inputs: {x}, backend, attrs: {perm: permutation}});
}
const permutedAxis = backend_util.getInnerMostAxes(1, x.shape.length)[0];
if (permutedAxis !== $x.shape.length - 1) {
throw new Error(
`backend.cumsum in CPU expects an inner-most ` +
`axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
}
const resultDtype = upcastType($x.dtype, 'int32');
const vals = util.makeZerosTypedArray(
util.sizeFromShape($x.shape), resultDtype) as TypedArray;
const aVals = backend.data.get($x.dataId).values as TypedArray;
const finalDim = $x.shape[$x.shape.length - 1];
const indexAdjuster = reverse ?
(i: number, j: number) => i + finalDim - j - 1 :
(i: number, j: number) => i + j;
for (let i = 0; i < aVals.length; i += finalDim) {
for (let j = 0; j < finalDim; j++) {
const idx = indexAdjuster(i, j);
if (j === 0) {
vals[idx] = exclusive ? 0 : aVals[idx];
} else {
const prevIdx = indexAdjuster(i, j - 1);
vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :
aVals[idx] + vals[prevIdx];
}
}
}
const result = backend.makeTensorInfo($x.shape, resultDtype, vals);
if (permutation != null) {
const reversePermutation = backend_util.getUndoAxesPermutation(permutation);
const reverseTransposedResult = transpose(
{inputs: {x: result}, backend, attrs: {perm: reversePermutation}});
backend.disposeIntermediateTensorInfo(result);
backend.disposeIntermediateTensorInfo($x);
return reverseTransposedResult;
}
return result;
}
export const cumsumConfig: KernelConfig = {
kernelName: Cumsum,
backendName: 'cpu',
kernelFunc: cumsum as unknown as KernelFunc
};