/**
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* @license
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* Copyright 2018 Google Inc. 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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import {ENGINE} from '../engine';
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import {Tensor, Tensor1D, Tensor2D, Tensor3D} from '../tensor';
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import {makeTypesMatch} from '../tensor_util';
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import {convertToTensor} from '../tensor_util_env';
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import {TensorLike} from '../types';
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import * as util from '../util';
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import {op} from './operation';
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/**
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* Computes the dot product of two matrices, A * B. These must be matrices.
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*
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* ```js
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* const a = tf.tensor2d([1, 2], [1, 2]);
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* const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* a.matMul(b).print(); // or tf.matMul(a, b)
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* ```
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* @param a First matrix in dot product operation.
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* @param b Second matrix in dot product operation.
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* @param transposeA If true, `a` is transposed before multiplication.
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* @param transposeB If true, `b` is transposed before multiplication.
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*/
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/** @doc {heading: 'Operations', subheading: 'Matrices'} */
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function matMul_<T extends Tensor>(
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a: T|TensorLike, b: T|TensorLike, transposeA = false,
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transposeB = false): T {
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let $a = convertToTensor(a, 'a', 'matMul');
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let $b = convertToTensor(b, 'b', 'matMul');
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[$a, $b] = makeTypesMatch($a, $b);
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const innerShapeA =
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transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
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const innerShapeB =
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transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
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const outerShapeA =
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transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
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const outerShapeB =
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transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
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const outerDimsA = $a.shape.slice(0, -2);
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const outerDimsB = $b.shape.slice(0, -2);
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const batchDimA = util.sizeFromShape(outerDimsA);
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const batchDimB = util.sizeFromShape(outerDimsB);
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util.assert(
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$a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,
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() => `Error in matMul: inputs must have the same rank of at least 2, ` +
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`got ranks ${$a.rank} and ${$b.rank}.`);
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util.assert(
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util.arraysEqual(outerDimsA, outerDimsB),
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() => `Error in matMul: outer dimensions (${outerDimsA}) and (` +
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`${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +
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`${$b.shape} must match.`);
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util.assert(
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innerShapeA === innerShapeB,
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() => `Error in matMul: inner shapes (${innerShapeA}) and (` +
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`${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +
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`${$b.shape} and transposeA=${transposeA}` +
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` and transposeB=${transposeB} must match.`);
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const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
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const a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
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$a.as3D(batchDimA, outerShapeA, innerShapeA);
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const b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
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$b.as3D(batchDimB, innerShapeB, outerShapeB);
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const grad = (dy: Tensor3D, saved: Tensor[]) => {
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const [a3D, b3D] = saved as Tensor3D[];
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if (!transposeA && !transposeB) {
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return {
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a: () => dy.matMul(b3D, false, true),
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b: () => a3D.matMul(dy, true, false)
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};
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} else if (!transposeA && transposeB) {
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return {
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a: () => dy.matMul(b3D, false, false),
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b: () => dy.matMul(a3D, true, false)
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};
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} else if (transposeA && !transposeB) {
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return {
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a: () => b3D.matMul(dy, false, true),
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b: () => a3D.matMul(dy, false, false)
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};
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} else {
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return {
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a: () => b3D.matMul(dy, true, true),
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b: () => dy.matMul(a3D, true, true)
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};
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}
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};
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const attrs = {transposeA, transposeB};
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const res = ENGINE.runKernelFunc((backend, save) => {
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const res = backend.batchMatMul(a3D, b3D, transposeA, transposeB);
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save([a3D, b3D]);
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return res;
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}, {a: a3D, b: b3D}, grad, 'BatchMatMul', attrs);
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return res.reshape(outShape) as T;
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}
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/**
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* Computes the outer product of two vectors, `v1` and `v2`.
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*
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* ```js
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* const a = tf.tensor1d([1, 2, 3]);
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* const b = tf.tensor1d([3, 4, 5]);
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*
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* tf.outerProduct(a, b).print();
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* ```
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* @param v1 The first vector in the outer product operation.
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* @param v2 The second vector in the outer product operation.
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*/
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/** @doc {heading: 'Operations', subheading: 'Matrices'} */
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function outerProduct_(
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v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {
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const $v1 = convertToTensor(v1, 'v1', 'outerProduct');
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const $v2 = convertToTensor(v2, 'v2', 'outerProduct');
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util.assert(
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$v1.rank === 1 && $v2.rank === 1,
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() => `Error in outerProduct: inputs must be rank 1, but got ranks ` +
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`${$v1.rank} and ${$v2.rank}.`);
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return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));
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}
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/**
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* Computes the dot product of two matrices and/or vectors, `t1` and `t2`.
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*
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* ```js
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* const a = tf.tensor1d([1, 2]);
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* const b = tf.tensor2d([[1, 2], [3, 4]]);
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* const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
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*
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* a.dot(b).print(); // or tf.dot(a, b)
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* b.dot(a).print();
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* b.dot(c).print();
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* ```
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* @param t1 The first tensor in the dot operation.
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* @param t2 The second tensor in the dot operation.
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*/
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/** @doc {heading: 'Operations', subheading: 'Matrices'} */
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function dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {
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const $t1 = convertToTensor(t1, 't1', 'dot');
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const $t2 = convertToTensor(t2, 't2', 'dot');
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util.assert(
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($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),
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() => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +
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`${$t1.rank} and ${$t2.rank}.`);
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const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);
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const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);
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util.assert(
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t1Inner === t2Inner,
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() => `Error in dot: inner dimensions of inputs must match, but got ` +
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`${t1Inner} and ${t2Inner}.`);
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if ($t1.rank === 1 && $t2.rank === 1) {
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return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();
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} else if ($t1.rank === 1 && $t2.rank === 2) {
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return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();
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} else if ($t1.rank === 2 && $t2.rank === 1) {
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return $t1.matMul($t2.as2D(-1, 1)).as1D();
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} else {
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return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));
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}
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}
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export const matMul = op({matMul_});
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export const dot = op({dot_});
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export const outerProduct = op({outerProduct_});
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