"use strict";
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/**
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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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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("../engine");
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var tensor_util_1 = require("../tensor_util");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var operation_1 = require("./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_(a, b, transposeA, transposeB) {
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var _a;
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if (transposeA === void 0) { transposeA = false; }
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if (transposeB === void 0) { transposeB = false; }
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'matMul');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'matMul');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
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var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
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var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
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var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
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var outerDimsA = $a.shape.slice(0, -2);
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var outerDimsB = $b.shape.slice(0, -2);
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var batchDimA = util.sizeFromShape(outerDimsA);
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var batchDimB = util.sizeFromShape(outerDimsB);
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util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, function () { return "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(util.arraysEqual(outerDimsA, outerDimsB), function () { return "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(innerShapeA === innerShapeB, function () { return "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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var outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
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var a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
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$a.as3D(batchDimA, outerShapeA, innerShapeA);
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var b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
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$b.as3D(batchDimB, innerShapeB, outerShapeB);
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var grad = function (dy, saved) {
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var _a = saved, a3D = _a[0], b3D = _a[1];
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if (!transposeA && !transposeB) {
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return {
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a: function () { return dy.matMul(b3D, false, true); },
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b: function () { return a3D.matMul(dy, true, false); }
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};
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}
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else if (!transposeA && transposeB) {
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return {
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a: function () { return dy.matMul(b3D, false, false); },
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b: function () { return dy.matMul(a3D, true, false); }
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};
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}
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else if (transposeA && !transposeB) {
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return {
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a: function () { return b3D.matMul(dy, false, true); },
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b: function () { return a3D.matMul(dy, false, false); }
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};
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}
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else {
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return {
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a: function () { return b3D.matMul(dy, true, true); },
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b: function () { return dy.matMul(a3D, true, true); }
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};
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}
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};
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var attrs = { transposeA: transposeA, transposeB: transposeB };
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var 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);
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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_(v1, v2) {
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var $v1 = tensor_util_env_1.convertToTensor(v1, 'v1', 'outerProduct');
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var $v2 = tensor_util_env_1.convertToTensor(v2, 'v2', 'outerProduct');
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util.assert($v1.rank === 1 && $v2.rank === 1, function () { return "Error in outerProduct: inputs must be rank 1, but got ranks " +
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($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, t2) {
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var $t1 = tensor_util_env_1.convertToTensor(t1, 't1', 'dot');
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var $t2 = tensor_util_env_1.convertToTensor(t2, 't2', 'dot');
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util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), function () { return "Error in dot: inputs must all be rank 1 or 2, but got ranks " +
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($t1.rank + " and " + $t2.rank + "."); });
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var t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);
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var t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);
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util.assert(t1Inner === t2Inner, function () { return "Error in dot: inner dimensions of inputs must match, but got " +
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(t1Inner + " and " + t2Inner + "."); });
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if ($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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}
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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], $t2.shape[1])).as1D();
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}
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else if ($t1.rank === 2 && $t2.rank === 1) {
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return $t1.matMul($t2.as2D(-1, 1)).as1D();
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}
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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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exports.matMul = operation_1.op({ matMul_: matMul_ });
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exports.dot = operation_1.op({ dot_: dot_ });
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exports.outerProduct = operation_1.op({ outerProduct_: outerProduct_ });
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//# sourceMappingURL=matmul.js.map
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