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broadcast_to.ts
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broadcast_to.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 {ENGINE} from '../engine';
import {Tile, TileAttrs, TileInputs} from '../kernel_names';
import {NamedAttrMap} from '../kernel_registry';
import {Tensor} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import {convertToTensor} from '../tensor_util_env';
import {Rank, ShapeMap, TensorLike} from '../types';
import {assertNonNegativeIntegerDimensions} from '../util_base';
import {clone} from './clone';
import {op} from './operation';
import {reshape} from './reshape';
/**
* Broadcast an array to a compatible shape NumPy-style.
*
* The tensor's shape is compared to the broadcast shape from end to beginning.
* Ones are prepended to the tensor's shape until it has the same length as
* the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
* already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
* the input tensor is tiled N times along that axis (using tf.tile).
*
* @param input The tensor that is to be broadcasted.
* @param shape The input is to be broadcast to this shape.
*
* @doc {heading: 'Tensors', subheading: 'Transformations'}
*/
function broadcastTo_<R extends Rank>(
x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {
let input = convertToTensor(x, 'broadcastTo', 'x');
const xShape = input.shape;
assertNonNegativeIntegerDimensions(shape);
if (shape.length < input.rank) {
throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${
input.rank}.`);
}
if (shape.length > input.rank) {
const newShape = input.shape.slice();
while (newShape.length < shape.length) {
newShape.unshift(1);
}
input = reshape(input, newShape);
}
const inputShape = input.shape;
const reps: number[] = Array.from(shape);
for (let i = shape.length - 1; i >= 0; i--) {
if (inputShape[i] === shape[i]) {
reps[i] = 1;
} else if (input.shape[i] !== 1) {
throw new Error(
`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
}
}
const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);
if (axes.length === 0) {
return clone(input) as Tensor<R>;
}
// TODO call broadcastTo kernel directly once backends implement broadcstTo
const inputs: TileInputs = {x: input};
const attrs: TileAttrs = {reps};
return ENGINE.runKernel(
Tile, inputs as unknown as NamedTensorMap,
attrs as unknown as NamedAttrMap);
}
export const broadcastTo = /* @__PURE__ */ op({broadcastTo_});