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modularize broadcastTo op (tensorflow#2919)
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,49 @@ | ||
| /** | ||
| * @license | ||
| * Copyright 2020 Google Inc. 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. | ||
| * ============================================================================= | ||
| */ | ||
|
|
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| import {BroadcastTo, BroadCastToAttrs} from '../kernel_names'; | ||
| import {GradConfig, NamedAttrMap} from '../kernel_registry'; | ||
| import {Tensor} from '../tensor'; | ||
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| export const broadcastToGradConfig: GradConfig = { | ||
| kernelName: BroadcastTo, | ||
| gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => { | ||
| const broadCastToAttrs: BroadCastToAttrs = | ||
| attrs as unknown as BroadCastToAttrs; | ||
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| const inputShape = broadCastToAttrs.inputShape; | ||
| const outputShape = broadCastToAttrs.shape; | ||
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| const reps: number[] = Array.from(outputShape); | ||
| for (let i = inputShape.length - 1; i >= 0; i--) { | ||
| if (inputShape[i] === outputShape[i]) { | ||
| reps[i] = 1; | ||
| } else if (inputShape[i] !== 1) { | ||
| throw new Error(`broadcastTo(): [${ | ||
| inputShape}] cannot be broadcast to [${outputShape}].`); | ||
| } | ||
| } | ||
| const axes: number[] = []; | ||
| for (let i = 0; i < reps.length; i++) { | ||
| if (reps[i] > 1) { | ||
| axes.push(i); | ||
| } | ||
| } | ||
| const keepDims = true; | ||
| return {x: () => dy.sum(axes, keepDims)}; | ||
| } | ||
| }; |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| /** | ||
| * @license | ||
| * Copyright 2020 Google Inc. 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 {KernelBackend} from '../backends/backend'; | ||
| import {ENGINE} from '../engine'; | ||
| import {BroadcastTo, BroadCastToAttrs, BroadcastToInputs} 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'; | ||
|
|
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| import {op} from './operation'; | ||
|
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| /** | ||
| * 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 is 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; | ||
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| if (shape.some(d => !(d > 0) || d % 1 !== 0)) { | ||
| throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`); | ||
| } | ||
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| if (shape.length < input.rank) { | ||
| throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${ | ||
| input.rank}.`); | ||
| } | ||
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| if (shape.length > input.rank) { | ||
| const newShape = input.shape.slice(); | ||
| while (newShape.length < shape.length) { | ||
| newShape.unshift(1); | ||
| } | ||
| input = input.reshape(newShape); | ||
| } | ||
|
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| const inputShape = input.shape; | ||
| const reps: number[] = Array.from(shape); | ||
| for (let i = shape.length - 1; i >= 0; i--) { | ||
| if (inputShape[i] === shape[i]) { | ||
| reps[i] = 1; | ||
| } else if (input.shape[i] !== 1) { | ||
| throw new Error( | ||
| `broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`); | ||
| } | ||
| } | ||
| const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0); | ||
|
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| if (axes.length === 0) { | ||
| return input.clone() as Tensor<R>; | ||
| } | ||
|
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| const forward = (backend: KernelBackend) => backend.tile(input, reps); | ||
| const keepDims = true; | ||
| const backward = (dy: Tensor) => ({x: () => dy.sum(axes, keepDims)}); | ||
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| const inputs: BroadcastToInputs = {x: input}; | ||
| const attrs: BroadCastToAttrs = {shape, inputShape}; | ||
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| return ENGINE.runKernelFunc( | ||
| forward, inputs as unknown as NamedTensorMap, backward, | ||
| BroadcastTo, attrs as unknown as NamedAttrMap) as Tensor<R>; | ||
| } | ||
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| export const broadcastTo = op({broadcastTo_}); |
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