"use strict";
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/**
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* @license
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* Copyright 2019 Google LLC. 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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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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var _this = this;
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("./index");
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var jasmine_util_1 = require("./jasmine_util");
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var test_util_1 = require("./test_util");
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jasmine_util_1.describeWithFlags('kernel_registry', jasmine_util_1.ALL_ENVS, function () {
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it('register a kernel and call it', function () {
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var called = false;
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tf.registerKernel({
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kernelName: 'MyKernel',
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backendName: tf.getBackend(),
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kernelFunc: function (_a) {
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var inputs = _a.inputs, attrs = _a.attrs;
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expect(attrs.a).toBe(5);
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expect(inputs.x.shape).toEqual([2, 2]);
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expect(inputs.x.dtype).toBe('float32');
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called = true;
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return { dtype: 'float32', shape: [3, 3], dataId: {} };
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}
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});
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var inputs = { x: tf.zeros([2, 2]) };
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var attrs = { a: 5 };
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var res = tf.engine().runKernel('MyKernel', inputs, attrs);
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expect(called).toBe(true);
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expect(res.dtype).toBe('float32');
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expect(res.shape).toEqual([3, 3]);
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tf.unregisterKernel('MyKernel', tf.getBackend());
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});
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it('errors when running non-existent kernel', function () {
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var inputs = {};
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var attrs = {};
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expect(function () { return tf.engine().runKernel('DoesNotExist', inputs, attrs); })
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.toThrowError();
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});
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it('errors when registering the same kernel twice', function () {
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tf.registerKernel({
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kernelName: 'MyKernel',
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backendName: tf.getBackend(),
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kernelFunc: function () {
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return null;
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}
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});
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expect(function () { return tf.registerKernel({
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kernelName: 'MyKernel',
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backendName: tf.getBackend(),
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kernelFunc: function () {
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return null;
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}
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}); }).toThrowError();
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tf.unregisterKernel('MyKernel', tf.getBackend());
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});
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it('register same kernel on two different backends', function () {
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tf.registerBackend('backend1', function () {
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return {
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id: 1,
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dispose: function () { return null; },
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disposeData: function (dataId) { return null; },
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numDataIds: function () { return 0; }
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};
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});
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tf.registerBackend('backend2', function () {
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return {
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id: 2,
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dispose: function () { return null; },
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disposeData: function (dataId) { return null; },
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numDataIds: function () { return 0; }
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};
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});
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var lastStorageId = -1;
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var kernelFunc = function (_a) {
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var backend = _a.backend;
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lastStorageId = backend.id;
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return { dataId: {}, shape: [], dtype: 'float32' };
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};
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tf.registerKernel({ kernelName: 'MyKernel', backendName: 'backend1', kernelFunc: kernelFunc });
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tf.registerKernel({ kernelName: 'MyKernel', backendName: 'backend2', kernelFunc: kernelFunc });
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// No kernel has been executed yet.
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expect(lastStorageId).toBe(-1);
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// Kernel was executed on the first backend.
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tf.setBackend('backend1');
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tf.engine().runKernel('MyKernel', {}, {});
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expect(lastStorageId).toBe(1);
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// Kernel was executed on the second backend.
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tf.setBackend('backend2');
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tf.engine().runKernel('MyKernel', {}, {});
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expect(lastStorageId).toBe(2);
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tf.removeBackend('backend1');
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tf.removeBackend('backend2');
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tf.unregisterKernel('MyKernel', 'backend1');
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tf.unregisterKernel('MyKernel', 'backend2');
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});
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it('register kernel with setup and dispose functions', function () {
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var backendName = 'custom-backend';
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var kernelName = 'MyKernel';
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var customBackend = {
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dispose: function () { return null; },
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disposeData: function (dataId) { return null; },
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numDataIds: function () { return 0; }
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};
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tf.registerBackend(backendName, function () { return customBackend; });
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var kernelFunc = function () {
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return { dataId: {}, shape: [], dtype: 'float32' };
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};
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var setupCalled = false;
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var setupFunc = function (backend) {
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expect(backend).toBe(customBackend);
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setupCalled = true;
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};
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var disposeCalled = false;
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var disposeFunc = function (backend) {
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expect(backend).toBe(customBackend);
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disposeCalled = true;
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};
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tf.registerKernel({ kernelName: kernelName, backendName: backendName, kernelFunc: kernelFunc, setupFunc: setupFunc, disposeFunc: disposeFunc });
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expect(setupCalled).toBe(false);
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expect(disposeCalled).toBe(false);
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tf.setBackend(backendName);
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expect(setupCalled).toBe(true);
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expect(disposeCalled).toBe(false);
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// Kernel was executed on the first backend.
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tf.engine().runKernel(kernelName, {}, {});
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tf.removeBackend(backendName);
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expect(setupCalled).toBe(true);
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expect(disposeCalled).toBe(true);
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tf.unregisterKernel(kernelName, backendName);
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});
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});
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jasmine_util_1.describeWithFlags('gradient registry', jasmine_util_1.ALL_ENVS, function () {
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it('register a kernel with gradient and call it', function () { return __awaiter(_this, void 0, void 0, function () {
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var kernelWasCalled, gradientWasCalled, kernelName, x, gradFunc, dx, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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kernelWasCalled = false;
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gradientWasCalled = false;
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kernelName = 'MyKernel';
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x = tf.zeros([2, 2]);
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tf.registerKernel({
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kernelName: kernelName,
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backendName: tf.getBackend(),
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kernelFunc: function () {
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kernelWasCalled = true;
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return { dtype: 'float32', shape: [3, 3], dataId: {} };
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}
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});
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tf.registerGradient({
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kernelName: kernelName,
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gradFunc: function (dy, saved) {
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// Make sure saved input (x) was passed to the gradient function.
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expect(saved[0].dataId).toEqual(x.dataId);
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// Make sure dy matches the shape of the output.
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expect(dy.shape).toEqual([3, 3]);
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gradientWasCalled = true;
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return { x: function () { return tf.fill([2, 2], 3); } };
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},
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});
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gradFunc = tf.grad(function (x) { return tf.engine().runKernel(kernelName, { x: x }, {} /* attrs */, [x] /* inputsToSave */); });
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dx = gradFunc(x);
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expect(kernelWasCalled).toBe(true);
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expect(gradientWasCalled).toBe(true);
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expect(dx.dtype).toBe('float32');
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expect(dx.shape).toEqual([2, 2]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, dx.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [3, 3, 3, 3]]);
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tf.unregisterKernel(kernelName, tf.getBackend());
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tf.unregisterGradient(kernelName);
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return [2 /*return*/];
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}
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});
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}); });
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it('errors when running non-existent gradient', function () {
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var kernelName = 'MyKernel';
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var x = tf.zeros([2, 2]);
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tf.registerKernel({
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kernelName: kernelName,
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backendName: tf.getBackend(),
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kernelFunc: function () { return ({ dtype: 'float32', shape: [3, 3], dataId: {} }); }
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});
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var gradFunc = tf.grad(function (x) { return tf.engine().runKernel(kernelName, { x: x }, {} /* attrs */, [x] /* inputsToSave */); });
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expect(function () { return gradFunc(x); })
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.toThrowError(/gradient function not found for MyKernel/);
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tf.unregisterKernel(kernelName, tf.getBackend());
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});
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it('warning when registering the same gradient twice', function () {
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var kernelName = 'MyKernel';
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tf.registerGradient({ kernelName: kernelName, gradFunc: function () { return null; } });
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spyOn(console, 'warn').and.callFake(function (msg) {
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expect(msg).toBe('Overriding the gradient for \'MyKernel\'');
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});
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tf.registerGradient({ kernelName: kernelName, gradFunc: function () { return null; } });
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tf.unregisterGradient(kernelName);
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});
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});
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//# sourceMappingURL=kernel_registry_test.js.map
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