"use strict";
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/**
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* @license
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* Copyright 2018 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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Object.defineProperty(exports, "__esModule", { value: true });
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var environment_1 = require("./environment");
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var kernel_registry_1 = require("./kernel_registry");
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var profiler_1 = require("./profiler");
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var tape_1 = require("./tape");
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var tensor_1 = require("./tensor");
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var tensor_util_1 = require("./tensor_util");
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var util = require("./util");
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var util_1 = require("./util");
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var EngineState = /** @class */ (function () {
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function EngineState() {
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// Public since optimizers will use it.
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this.registeredVariables = {};
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this.nextTapeNodeId = 0;
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this.numBytes = 0;
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this.numTensors = 0;
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this.numStringTensors = 0;
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this.numDataBuffers = 0;
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// Number of nested tf.grad() statements when computing higher-order
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// gradients. E.g. `1` for first-order gradients and `2` for second-order
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// gradients. Used to track if the tape should be removed after a backprop.
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this.gradientDepth = 0;
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// Number of nested kernel calls. When kernel depth is greater than 1, we turn
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// off the tape.
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this.kernelDepth = 0;
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this.scopeStack = [];
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/**
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* Keeps track of the number of data moves during a kernel execution. We
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* maintain a stack since kernels can call other kernels, recursively.
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*/
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this.numDataMovesStack = [];
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this.nextScopeId = 0;
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this.tensorInfo = new WeakMap();
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this.profiling = false;
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this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };
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}
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EngineState.prototype.dispose = function () {
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for (var variableName in this.registeredVariables) {
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this.registeredVariables[variableName].dispose();
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}
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};
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return EngineState;
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}());
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var Engine = /** @class */ (function () {
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function Engine(ENV) {
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this.ENV = ENV;
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this.registry = {};
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this.registryFactory = {};
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this.pendingBackendInitId = 0;
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this.state = new EngineState();
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}
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Engine.prototype.ready = function () {
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return __awaiter(this, void 0, void 0, function () {
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var sortedBackends, i, backendName, success;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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if (this.pendingBackendInit != null) {
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return [2 /*return*/, this.pendingBackendInit.then(function () { })];
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}
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if (this.backendInstance != null) {
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return [2 /*return*/];
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}
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sortedBackends = this.getSortedBackends();
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i = 0;
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_a.label = 1;
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case 1:
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if (!(i < sortedBackends.length)) return [3 /*break*/, 5];
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backendName = sortedBackends[i];
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return [4 /*yield*/, this.initializeBackend(backendName).success];
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case 2:
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success = _a.sent();
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if (!success) return [3 /*break*/, 4];
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return [4 /*yield*/, this.setBackend(backendName)];
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case 3:
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_a.sent();
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return [2 /*return*/];
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case 4:
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i++;
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return [3 /*break*/, 1];
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case 5: throw new Error("Could not initialize any backends, all backend initializations " +
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"failed.");
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}
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});
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});
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};
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Object.defineProperty(Engine.prototype, "backend", {
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get: function () {
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if (this.pendingBackendInit != null) {
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throw new Error("Backend '" + this.backendName + "' has not yet been initialized. Make " +
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"sure to await tf.ready() or await tf.setBackend() before calling " +
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"other methods");
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}
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if (this.backendInstance == null) {
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var _a = this.initializeBackendsAndReturnBest(), name_1 = _a.name, asyncInit = _a.asyncInit;
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if (asyncInit) {
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throw new Error("The highest priority backend '" + name_1 + "' has not yet been " +
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"initialized. Make sure to await tf.ready() or " +
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"await tf.setBackend() before calling other methods");
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}
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this.setBackend(name_1);
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}
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return this.backendInstance;
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},
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enumerable: true,
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configurable: true
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});
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Engine.prototype.backendNames = function () {
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return Object.keys(this.registryFactory);
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};
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Engine.prototype.findBackend = function (backendName) {
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if (!(backendName in this.registry)) {
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// If the backend hasn't been initialized but we have a registry entry for
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// it, initialize it and return it.
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if (backendName in this.registryFactory) {
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var asyncInit = this.initializeBackend(backendName).asyncInit;
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if (asyncInit) {
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// Backend is not ready yet.
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return null;
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}
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}
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else {
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return null;
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}
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}
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return this.registry[backendName];
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};
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Engine.prototype.findBackendFactory = function (backendName) {
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if (!(backendName in this.registryFactory)) {
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return null;
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}
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return this.registryFactory[backendName].factory;
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};
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Engine.prototype.registerBackend = function (backendName, factory, priority) {
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if (priority === void 0) { priority = 1; }
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if (backendName in this.registryFactory) {
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console.warn(backendName + " backend was already registered. " +
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"Reusing existing backend factory.");
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return false;
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}
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this.registryFactory[backendName] = { factory: factory, priority: priority };
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return true;
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};
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Engine.prototype.setBackend = function (backendName) {
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return __awaiter(this, void 0, void 0, function () {
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var _a, success, asyncInit, result, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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if (this.registryFactory[backendName] == null) {
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throw new Error("Backend name '" + backendName + "' not found in registry");
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}
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this.backendName = backendName;
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if (!(this.registry[backendName] == null)) return [3 /*break*/, 4];
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this.backendInstance = null;
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_a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit;
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if (!asyncInit) return [3 /*break*/, 2];
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return [4 /*yield*/, success];
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case 1:
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_b = _c.sent();
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return [3 /*break*/, 3];
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case 2:
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_b = success;
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_c.label = 3;
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case 3:
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result = _b;
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if (!result) {
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return [2 /*return*/, false];
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}
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_c.label = 4;
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case 4:
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this.backendInstance = this.registry[backendName];
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this.setupRegisteredKernels();
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// Reset the profiler.
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this.profiler = new profiler_1.Profiler(this.backendInstance);
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return [2 /*return*/, true];
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}
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});
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});
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};
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Engine.prototype.setupRegisteredKernels = function () {
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var _this = this;
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var kernels = kernel_registry_1.getKernelsForBackend(this.backendName);
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kernels.forEach(function (kernel) {
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if (kernel.setupFunc != null) {
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kernel.setupFunc(_this.backendInstance);
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}
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});
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};
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Engine.prototype.disposeRegisteredKernels = function (backendName) {
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var _this = this;
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var kernels = kernel_registry_1.getKernelsForBackend(backendName);
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kernels.forEach(function (kernel) {
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if (kernel.disposeFunc != null) {
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kernel.disposeFunc(_this.registry[backendName]);
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}
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});
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};
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/**
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* Initializes a backend by looking up the backend name in the factory
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* registry and calling the factory method. Returns a boolean representing
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* whether the initialization of the backend suceeded. Throws an error if
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* there is no backend in the factory registry.
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*/
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Engine.prototype.initializeBackend = function (backendName) {
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var _this = this;
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var registryFactoryEntry = this.registryFactory[backendName];
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if (registryFactoryEntry == null) {
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throw new Error("Cannot initialize backend " + backendName + ", no registration found.");
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}
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try {
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var backend = registryFactoryEntry.factory();
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// Test if the factory returns a promise.
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if (Promise.resolve(backend) === backend) {
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var promiseId_1 = ++this.pendingBackendInitId;
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var success = backend
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.then(function (backendInstance) {
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// Outdated promise. Another backend was set in the meantime.
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if (promiseId_1 < _this.pendingBackendInitId) {
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return false;
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}
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_this.registry[backendName] = backendInstance;
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_this.pendingBackendInit = null;
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return true;
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})
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.catch(function (err) {
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// Outdated promise. Another backend was set in the meantime.
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if (promiseId_1 < _this.pendingBackendInitId) {
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return false;
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}
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_this.pendingBackendInit = null;
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console.warn("Initialization of backend " + backendName + " failed");
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console.warn(err.stack || err.message);
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return false;
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});
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this.pendingBackendInit = success;
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return { success: success, asyncInit: true };
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}
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else {
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this.registry[backendName] = backend;
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return { success: true, asyncInit: false };
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}
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}
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catch (err) {
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console.warn("Initialization of backend " + backendName + " failed");
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console.warn(err.stack || err.message);
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return { success: false, asyncInit: false };
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}
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};
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Engine.prototype.removeBackend = function (backendName) {
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if (!(backendName in this.registryFactory)) {
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throw new Error(backendName + " backend not found in registry");
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}
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if (this.backendName === backendName && this.pendingBackendInit != null) {
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// There is a pending promise of the backend we want to remove. Make it
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// obsolete.
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this.pendingBackendInitId++;
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}
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if (backendName in this.registry) {
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this.disposeRegisteredKernels(backendName);
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this.registry[backendName].dispose();
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delete this.registry[backendName];
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}
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delete this.registryFactory[backendName];
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// Unset the backend if it is active.
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if (this.backendName === backendName) {
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this.pendingBackendInit = null;
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this.backendName = null;
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this.backendInstance = null;
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}
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};
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Engine.prototype.getSortedBackends = function () {
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var _this = this;
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if (Object.keys(this.registryFactory).length === 0) {
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throw new Error('No backend found in registry.');
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}
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return Object.keys(this.registryFactory).sort(function (a, b) {
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// Highest priority comes first.
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return _this.registryFactory[b].priority -
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_this.registryFactory[a].priority;
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});
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};
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Engine.prototype.initializeBackendsAndReturnBest = function () {
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var sortedBackends = this.getSortedBackends();
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for (var i = 0; i < sortedBackends.length; i++) {
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var backendName = sortedBackends[i];
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var _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit;
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if (asyncInit || success) {
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return { name: backendName, asyncInit: asyncInit };
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}
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}
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throw new Error("Could not initialize any backends, all backend initializations " +
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"failed.");
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};
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Engine.prototype.moveData = function (destBackend, dataId) {
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var info = this.state.tensorInfo.get(dataId);
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var srcBackend = info.backend;
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var values = this.readSync(dataId);
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// Delete the tensor from the old backend and move it to the new
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// backend.
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srcBackend.disposeData(dataId);
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info.backend = destBackend;
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destBackend.move(dataId, values, info.shape, info.dtype);
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if (this.shouldCheckForMemLeaks()) {
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// Track the number of moves during a kernel execution to correctly
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// detect memory leaks.
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this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
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}
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};
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Engine.prototype.tidy = function (nameOrFn, fn) {
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var _this = this;
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var name = null;
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if (fn == null) {
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// Called with only 1 argument.
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if (typeof nameOrFn !== 'function') {
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throw new Error('Please provide a function to tidy()');
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}
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fn = nameOrFn;
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}
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else {
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// Called with 2 arguments.
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if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {
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throw new Error('When calling with two arguments, the first argument ' +
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'to tidy() must be a string');
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}
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if (typeof fn !== 'function') {
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throw new Error('When calling with two arguments, the 2nd argument ' +
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'to tidy() must be a function');
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}
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name = nameOrFn;
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// TODO(nsthorat,smilkov): Do operation logging and performance
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// profiling.
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}
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var result;
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return this.scopedRun(function () { return _this.startScope(name); }, function () { return _this.endScope(result); }, function () {
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result = fn();
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if (result instanceof Promise) {
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console.error('Cannot return a Promise inside of tidy.');
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}
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return result;
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});
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};
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Engine.prototype.scopedRun = function (start, end, f) {
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start();
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try {
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var res = f();
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end();
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return res;
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}
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catch (ex) {
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end();
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throw ex;
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}
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};
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Engine.prototype.nextTensorId = function () {
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return Engine.nextTensorId++;
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};
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Engine.prototype.nextVariableId = function () {
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return Engine.nextVariableId++;
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};
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/**
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* This method is called instead of the public-facing tensor.clone() when
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* saving a tensor for backwards pass. It makes sure to add the clone
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* operation to the tape regardless of being called inside a kernel
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* execution.
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*
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* This method will go away once all kernels are modularized since we won't
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* need to turn off the tape inside runKernel().
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*/
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Engine.prototype.clone = function (x) {
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var y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
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var inputs = { x: x };
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var grad = function (dy) { return ({ x: function () { return dy.toFloat(); } }); };
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var saved = [];
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this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved);
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return y;
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};
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/**
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* Execute a kernel with the given name and return the output tensor.
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*
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* @param kernelName The name of the kernel to execute.
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* @param inputs A map of input names to tensors.
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* @param attrs A map of attribute names to their values. An attribute is a
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* primitive (non-tensor) input to the kernel.
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* @param inputsToSave A list of tensors, inputs to save for the backprop
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* computation.
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* @param outputsToSave A list of booleans, specifying which output to save
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* for the backprop computation. These are booleans since the output
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* tensors are not visible to the user.
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*/
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Engine.prototype.runKernel = function (kernelName, inputs, attrs, inputsToSave, outputsToSave) {
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var forwardFunc = null;
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var backwardsFunc = null;
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// Call runKernel as a stop-gap until we modularize all kernels.
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// Once we modularize all kernels, we will remove the existing
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// `runKernelFunc`.
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return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
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};
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Engine.prototype.shouldCheckForMemLeaks = function () {
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return this.ENV.getBool('IS_TEST');
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};
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Engine.prototype.checkKernelForMemLeak = function (kernelName, numDataIdsBefore, outInfos) {
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var numDataIdsAfter = this.backend.numDataIds();
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// Count the number of data ids associated with the result of the kernel.
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var numOutputDataIds = 0;
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outInfos.forEach(function (info) {
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// Complex numbers allocate 3 data ids, one for 'real', one for
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// 'imaginary', and one for the container that holds the former two.
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numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);
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});
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// Account for the number of moves during kernel execution. A "data move"
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// can happen in the middle of a kernel execution, placing a new (key,value)
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// pair in the data storage. Since data moves have net zero effect (we
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// always remove the data from the old backend), we have to cancel them out
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// when detecting memory leaks.
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var numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
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var dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
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if (dataIdsLeaked > 0) {
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throw new Error("Backend '" + this.backendName + "' has an internal memory leak " +
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("(" + dataIdsLeaked + " data ids) after running '" + kernelName + "'"));
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}
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};
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/**
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* @deprecated Use `runKernel` for newly added kernels. Keep using this method
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* only for kernels that are not yet fully modularized.
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*/
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Engine.prototype.runKernelFunc = function (forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
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var _this = this;
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if (inputsToSave === void 0) { inputsToSave = []; }
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if (outputsToSave === void 0) { outputsToSave = []; }
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var outputs;
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var saved = [];
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var isTapeOn = this.isTapeOn();
|
if (kernelName == null) {
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kernelName =
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this.state.activeScope != null ? this.state.activeScope.name : '';
|
}
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var saveFunc = function (tensors) {
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// Do not save unless we are recording to the tape. Otherwise it would
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// cause a mem leak since we would never run backprop, which disposes
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// the kept tensors.
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if (!isTapeOn) {
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return;
|
}
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saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); });
|
};
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var startingBytecount = this.state.numBytes;
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var startingNumTensors = this.state.numTensors;
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if (this.shouldCheckForMemLeaks()) {
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this.state.numDataMovesStack.push(0);
|
}
|
var kernelFunc;
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var kernel = kernel_registry_1.getKernel(kernelName, this.backendName);
|
var out;
|
if (kernel != null) {
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kernelFunc = function () {
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var numDataIdsBefore = _this.backend.numDataIds();
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out = kernel.kernelFunc({ inputs: inputs, attrs: attrs, backend: _this.backend });
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var outInfos = Array.isArray(out) ? out : [out];
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if (_this.shouldCheckForMemLeaks()) {
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_this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
|
}
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var outTensors = outInfos.map(function (_a) {
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var dataId = _a.dataId, shape = _a.shape, dtype = _a.dtype;
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return _this.makeTensorFromDataId(dataId, shape, dtype);
|
});
|
var outsToSave = outTensors.filter(function (_, i) { return outputsToSave[i]; });
|
// Save the inputs and outputs.
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saveFunc((inputsToSave || []).slice().concat(outsToSave));
|
return outTensors;
|
};
|
}
|
else {
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kernelFunc = function () {
|
var numDataIdsBefore = _this.backend.numDataIds();
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out = _this.tidy(function () { return forwardFunc(_this.backend, saveFunc); });
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var outs = (Array.isArray(out) ? out : [out]);
|
if (_this.shouldCheckForMemLeaks()) {
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_this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
|
}
|
return outs;
|
};
|
}
|
// Stop recording to a tape when running a kernel.
|
this.scopedRun(function () { return _this.state.kernelDepth++; }, function () { return _this.state.kernelDepth--; }, function () {
|
if (!_this.ENV.getBool('DEBUG')) {
|
outputs = kernelFunc();
|
}
|
else {
|
outputs = _this.profiler.profileKernel(kernelName, inputs, function () { return kernelFunc(); });
|
}
|
});
|
if (isTapeOn) {
|
this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved);
|
}
|
if (this.state.profiling) {
|
this.state.activeProfile.kernels.push({
|
name: kernelName,
|
bytesAdded: this.state.numBytes - startingBytecount,
|
totalBytesSnapshot: this.state.numBytes,
|
tensorsAdded: this.state.numTensors - startingNumTensors,
|
totalTensorsSnapshot: this.state.numTensors,
|
inputShapes: Object.keys(inputs).map(function (key) { return inputs[key].shape; }),
|
outputShapes: outputs.map(function (item) { return item.shape; })
|
});
|
}
|
return (Array.isArray(out) ? outputs : outputs[0]);
|
};
|
/**
|
* Internal method used by public APIs for tensor creation. Makes a new
|
* tensor with the provided shape, dtype and values. It always
|
* creates a new data id and writes the values to the underlying backend.
|
*/
|
Engine.prototype.makeTensor = function (values, shape, dtype, backend) {
|
if (values == null) {
|
throw new Error('Values passed to engine.makeTensor() are null');
|
}
|
dtype = dtype || 'float32';
|
backend = backend || this.backend;
|
var backendVals = values;
|
if (dtype === 'string' && util.isString(values[0])) {
|
backendVals = values.map(function (d) { return util.encodeString(d); });
|
}
|
var dataId = backend.write(backendVals, shape, dtype);
|
var t = new tensor_1.Tensor(shape, dtype, dataId, this.nextTensorId());
|
this.incRef(t, backend);
|
// Count bytes for string tensors.
|
if (dtype === 'string') {
|
var info = this.state.tensorInfo.get(dataId);
|
var newBytes = util_1.bytesFromStringArray(backendVals);
|
this.state.numBytes += newBytes - info.bytes;
|
info.bytes = newBytes;
|
}
|
return t;
|
};
|
/**
|
* Internal method used by backends. Makes a new tensor
|
* that is a wrapper around an existing data id. It doesn't create
|
* a new data id, only increments the ref count used in memory tracking.
|
*/
|
Engine.prototype.makeTensorFromDataId = function (dataId, shape, dtype, backend) {
|
dtype = dtype || 'float32';
|
var t = new tensor_1.Tensor(shape, dtype, dataId, this.nextTensorId());
|
this.incRef(t, backend);
|
return t;
|
};
|
Engine.prototype.makeVariable = function (initialValue, trainable, name, dtype) {
|
if (trainable === void 0) { trainable = true; }
|
name = name || this.nextVariableId().toString();
|
if (dtype != null && dtype !== initialValue.dtype) {
|
initialValue = initialValue.asType(dtype);
|
}
|
var v = new tensor_1.Variable(initialValue, trainable, name, this.nextTensorId());
|
if (this.state.registeredVariables[v.name] != null) {
|
throw new Error("Variable with name " + v.name + " was already registered");
|
}
|
this.state.registeredVariables[v.name] = v;
|
this.incRef(v, this.backend);
|
return v;
|
};
|
Engine.prototype.incRef = function (a, backend) {
|
var refCount = this.state.tensorInfo.has(a.dataId) ?
|
this.state.tensorInfo.get(a.dataId).refCount :
|
0;
|
this.state.numTensors++;
|
if (a.dtype === 'string') {
|
this.state.numStringTensors++;
|
}
|
if (refCount === 0) {
|
this.state.numDataBuffers++;
|
// Bytes for complex numbers are counted by their components. Bytes for
|
// string tensors are counted when writing values.
|
var bytes = 0;
|
if (a.dtype !== 'complex64' && a.dtype !== 'string') {
|
bytes = a.size * util.bytesPerElement(a.dtype);
|
}
|
this.state.tensorInfo.set(a.dataId, {
|
backend: backend || this.backend,
|
dtype: a.dtype,
|
shape: a.shape,
|
bytes: bytes,
|
refCount: 0
|
});
|
this.state.numBytes += bytes;
|
}
|
this.state.tensorInfo.get(a.dataId).refCount++;
|
if (!(a instanceof tensor_1.Variable)) {
|
this.track(a);
|
}
|
};
|
Engine.prototype.disposeTensor = function (a) {
|
if (!this.state.tensorInfo.has(a.dataId)) {
|
return;
|
}
|
this.state.numTensors--;
|
if (a.dtype === 'string') {
|
this.state.numStringTensors--;
|
}
|
var info = this.state.tensorInfo.get(a.dataId);
|
var refCount = info.refCount;
|
if (refCount <= 1) {
|
// Don't count bytes for complex numbers as they are counted by their
|
// components.
|
if (a.dtype !== 'complex64') {
|
this.state.numBytes -= info.bytes;
|
}
|
this.state.numDataBuffers--;
|
info.backend.disposeData(a.dataId);
|
this.state.tensorInfo.delete(a.dataId);
|
}
|
else {
|
this.state.tensorInfo.get(a.dataId).refCount--;
|
}
|
// TODO(nsthorat): Construct an error and save the stack trace for
|
// debugging when in debug mode. Creating a stack trace is too expensive
|
// to do unconditionally.
|
};
|
Engine.prototype.disposeVariables = function () {
|
for (var varName in this.state.registeredVariables) {
|
var v = this.state.registeredVariables[varName];
|
this.disposeVariable(v);
|
}
|
};
|
Engine.prototype.disposeVariable = function (v) {
|
this.disposeTensor(v);
|
if (this.state.registeredVariables[v.name] != null) {
|
delete this.state.registeredVariables[v.name];
|
}
|
};
|
Engine.prototype.memory = function () {
|
var info = this.backend.memory();
|
info.numTensors = this.state.numTensors;
|
info.numDataBuffers = this.state.numDataBuffers;
|
info.numBytes = this.state.numBytes;
|
if (this.state.numStringTensors > 0) {
|
info.unreliable = true;
|
if (info.reasons == null) {
|
info.reasons = [];
|
}
|
info.reasons.push('Memory usage by string tensors is approximate ' +
|
'(2 bytes per character)');
|
}
|
return info;
|
};
|
Engine.prototype.profile = function (query) {
|
return __awaiter(this, void 0, void 0, function () {
|
var startBytes, startNumTensors;
|
return __generator(this, function (_a) {
|
this.state.profiling = true;
|
startBytes = this.state.numBytes;
|
startNumTensors = this.state.numTensors;
|
this.state.activeProfile.kernels = [];
|
this.state.activeProfile.result = query();
|
this.state.profiling = false;
|
this.state.activeProfile.peakBytes = Math.max.apply(Math, this.state.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }));
|
this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
|
this.state.activeProfile.newTensors =
|
this.state.numTensors - startNumTensors;
|
return [2 /*return*/, this.state.activeProfile];
|
});
|
});
|
};
|
Engine.prototype.isTapeOn = function () {
|
return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
|
};
|
Engine.prototype.addTapeNode = function (kernelName, inputs, outputs, gradientsFunc, saved) {
|
var _this = this;
|
var tapeNode = { id: this.state.nextTapeNodeId++, kernelName: kernelName, inputs: inputs, outputs: outputs, saved: saved };
|
var gradConfig = kernel_registry_1.getGradient(kernelName);
|
if (gradConfig != null) {
|
gradientsFunc = gradConfig.gradFunc;
|
}
|
if (gradientsFunc != null) {
|
tapeNode.gradient = function (dys) {
|
// TODO(smilkov): To optimize back-prop, pass dys that are not used in
|
// the backprop graph to the user as null instead of zeros
|
dys = dys.map(function (dy, i) {
|
if (dy == null) {
|
var output = outputs[i];
|
var vals = util.makeZerosTypedArray(output.size, output.dtype);
|
return _this.makeTensor(vals, output.shape, output.dtype);
|
}
|
return dy;
|
});
|
// Grad functions of ops with single outputs expect a dy, while ops
|
// with multiple outputs expect dys (array of dy).
|
return gradientsFunc(dys.length > 1 ? dys : dys[0], saved);
|
};
|
}
|
this.state.activeTape.push(tapeNode);
|
};
|
Engine.prototype.keep = function (result) {
|
result.kept = true;
|
return result;
|
};
|
Engine.prototype.startTape = function () {
|
if (this.state.gradientDepth === 0) {
|
this.state.activeTape = [];
|
}
|
this.state.gradientDepth++;
|
};
|
Engine.prototype.endTape = function () {
|
this.state.gradientDepth--;
|
};
|
/**
|
* Start a scope. Use this with endScope() to achieve the same functionality
|
* as scope() without the need for a function closure.
|
*/
|
Engine.prototype.startScope = function (name) {
|
var scopeInfo = {
|
track: [],
|
name: 'unnamed scope',
|
id: this.state.nextScopeId++
|
};
|
if (name) {
|
scopeInfo.name = name;
|
}
|
this.state.scopeStack.push(scopeInfo);
|
this.state.activeScope = scopeInfo;
|
};
|
/**
|
* End a scope. Use this with startScope() to achieve the same functionality
|
* as scope() without the need for a function closure.
|
*/
|
Engine.prototype.endScope = function (result) {
|
var _this = this;
|
var tensorsToTrackInParent = tensor_util_1.getTensorsInContainer(result);
|
var tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(function (t) { return t.id; }));
|
// Dispose the arrays tracked in this scope.
|
for (var i = 0; i < this.state.activeScope.track.length; i++) {
|
var tensor = this.state.activeScope.track[i];
|
if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {
|
tensor.dispose();
|
}
|
}
|
var oldScope = this.state.scopeStack.pop();
|
this.state.activeScope = this.state.scopeStack.length === 0 ?
|
null :
|
this.state.scopeStack[this.state.scopeStack.length - 1];
|
// Track the current result in the parent scope.
|
tensorsToTrackInParent.forEach(function (tensor) {
|
// Only track the tensor if was allocated in the inner scope and is not
|
// globally kept.
|
if (!tensor.kept && tensor.scopeId === oldScope.id) {
|
_this.track(tensor);
|
}
|
});
|
};
|
/**
|
* Returns gradients of `f` with respect to each of the `xs`. The gradients
|
* returned are of the same length as `xs`, but some might be null if `f`
|
* was not a function of that `x`. It also takes optional dy to multiply the
|
* gradient, which defaults to `1`.
|
*/
|
Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) {
|
var _this = this;
|
if (allowNoGradients === void 0) { allowNoGradients = false; }
|
util.assert(xs.length > 0, function () { return 'gradients() received an empty list of xs.'; });
|
if (dy != null && dy.dtype !== 'float32') {
|
throw new Error("dy must have 'float32' dtype, but has '" + dy.dtype + "'");
|
}
|
var y = this.scopedRun(function () { return _this.startTape(); }, function () { return _this.endTape(); }, function () { return _this.tidy('forward', f); });
|
util.assert(y instanceof tensor_1.Tensor, function () { return 'The result y returned by f() must be a tensor.'; });
|
// Filter out the nodes that don't connect x => y.
|
var filteredTape = tape_1.getFilteredNodesXToY(this.state.activeTape, xs, y);
|
if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
|
throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +
|
'that the f you passed encloses all operations that lead from x ' +
|
'to y.');
|
}
|
return this.tidy('backward', function () {
|
var accumulatedGradientMap = {};
|
accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;
|
// Backprop gradients through the filtered nodes.
|
tape_1.backpropagateGradients(accumulatedGradientMap, filteredTape,
|
// Pass the tidy function to avoid circular dep with `tape.ts`.
|
function (f) { return _this.tidy(f); });
|
var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; });
|
if (_this.state.gradientDepth === 0) {
|
// This means that we are not computing higher-order gradients
|
// and can clean up the tape.
|
_this.state.activeTape.forEach(function (node) {
|
for (var _i = 0, _a = node.saved; _i < _a.length; _i++) {
|
var tensor = _a[_i];
|
tensor.dispose();
|
}
|
});
|
_this.state.activeTape = null;
|
}
|
return { value: y, grads: grads };
|
});
|
};
|
Engine.prototype.customGrad = function (f) {
|
var _this = this;
|
util.assert(util.isFunction(f), function () { return 'The f passed in customGrad(f) must be a function.'; });
|
return function () {
|
var inputs = [];
|
for (var _i = 0; _i < arguments.length; _i++) {
|
inputs[_i] = arguments[_i];
|
}
|
util.assert(inputs.every(function (t) { return t instanceof tensor_1.Tensor; }), function () { return 'The args passed in customGrad(f)(x1, x2,...) must all be ' +
|
'tensors'; });
|
var res;
|
var inputMap = {};
|
inputs.forEach(function (input, i) {
|
inputMap[i] = input;
|
});
|
return _this.runKernelFunc(function (_, save) {
|
res = f.apply(void 0, inputs.concat([save]));
|
util.assert(res.value instanceof tensor_1.Tensor, function () { return 'The function f passed in customGrad(f) must return an ' +
|
'object where `obj.value` is a tensor'; });
|
util.assert(util.isFunction(res.gradFunc), function () { return 'The function f passed in customGrad(f) must return an ' +
|
'object where `obj.gradFunc` is a function.'; });
|
return res.value;
|
}, inputMap, function (dy, saved) {
|
var gradRes = res.gradFunc(dy, saved);
|
var grads = Array.isArray(gradRes) ? gradRes : [gradRes];
|
util.assert(grads.length === inputs.length, function () { return 'The function f passed in customGrad(f) must return an ' +
|
'object where `obj.gradFunc` is a function that returns ' +
|
'the same number of tensors as inputs passed to f(...).'; });
|
util.assert(grads.every(function (t) { return t instanceof tensor_1.Tensor; }), function () { return 'The function f passed in customGrad(f) must return an ' +
|
'object where `obj.gradFunc` is a function that returns ' +
|
'a list of only tensors.'; });
|
var gradMap = {};
|
grads.forEach(function (grad, i) {
|
gradMap[i] = function () { return grad; };
|
});
|
return gradMap;
|
});
|
};
|
};
|
Engine.prototype.readSync = function (dataId) {
|
// Route the read to the correct backend.
|
var info = this.state.tensorInfo.get(dataId);
|
return info.backend.readSync(dataId);
|
};
|
Engine.prototype.read = function (dataId) {
|
// Route the read to the correct backend.
|
var info = this.state.tensorInfo.get(dataId);
|
return info.backend.read(dataId);
|
};
|
Engine.prototype.time = function (query) {
|
return __awaiter(this, void 0, void 0, function () {
|
var start, timingInfo;
|
return __generator(this, function (_a) {
|
switch (_a.label) {
|
case 0:
|
start = util_1.now();
|
return [4 /*yield*/, this.backend.time(query)];
|
case 1:
|
timingInfo = _a.sent();
|
timingInfo.wallMs = util_1.now() - start;
|
return [2 /*return*/, timingInfo];
|
}
|
});
|
});
|
};
|
/**
|
* Tracks a Tensor in the current scope to be automatically cleaned up
|
* when the current scope ends, and returns the value.
|
*
|
* @param result The Tensor to track in the current scope.
|
*/
|
Engine.prototype.track = function (result) {
|
if (this.state.activeScope != null) {
|
result.scopeId = this.state.activeScope.id;
|
this.state.activeScope.track.push(result);
|
}
|
return result;
|
};
|
Object.defineProperty(Engine.prototype, "registeredVariables", {
|
get: function () {
|
return this.state.registeredVariables;
|
},
|
enumerable: true,
|
configurable: true
|
});
|
/**
|
* Resets the engine state. Removes all backends but does not remove
|
* registered backend factories.
|
*/
|
Engine.prototype.reset = function () {
|
// Make any pending promise obsolete.
|
this.pendingBackendInitId++;
|
this.state.dispose();
|
this.ENV.reset();
|
this.state = new EngineState();
|
for (var backendName in this.registry) {
|
this.disposeRegisteredKernels(backendName);
|
this.registry[backendName].dispose();
|
delete this.registry[backendName];
|
}
|
this.backendName = null;
|
this.backendInstance = null;
|
this.pendingBackendInit = null;
|
};
|
Engine.nextTensorId = 0;
|
Engine.nextVariableId = 0;
|
return Engine;
|
}());
|
exports.Engine = Engine;
|
function ones(shape) {
|
var values = util_1.makeOnesTypedArray(util_1.sizeFromShape(shape), 'float32');
|
return exports.ENGINE.makeTensor(values, shape, 'float32');
|
}
|
var GLOBAL;
|
function getGlobalNamespace() {
|
if (GLOBAL == null) {
|
// tslint:disable-next-line:no-any
|
var ns = void 0;
|
if (typeof (window) !== 'undefined') {
|
ns = window;
|
}
|
else if (typeof (global) !== 'undefined') {
|
ns = global;
|
}
|
else if (typeof (process) !== 'undefined') {
|
ns = process;
|
}
|
else if (typeof (self) !== 'undefined') {
|
ns = self;
|
}
|
else {
|
throw new Error('Could not find a global object');
|
}
|
GLOBAL = ns;
|
}
|
return GLOBAL;
|
}
|
function getOrMakeEngine() {
|
var ns = getGlobalNamespace();
|
if (ns._tfengine == null) {
|
var environment = new environment_1.Environment(ns);
|
ns._tfengine = new Engine(environment);
|
}
|
environment_1.setEnvironmentGlobal(ns._tfengine.ENV);
|
// Tell the current tensor interface that the global engine is responsible
|
// for tracking.
|
tensor_1.setTensorTracker(function () { return ns._tfengine; });
|
return ns._tfengine;
|
}
|
exports.ENGINE = getOrMakeEngine();
|
//# sourceMappingURL=engine.js.map
|
