@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
324 lines • 14.3 kB
JavaScript
"use strict";
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = y[op[0] & 2 ? "return" : op[0] ? "throw" : "next"]) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [0, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var environment_1 = require("./environment");
var globals_1 = require("./globals");
var ops = require("./ops/ops");
var profiler_1 = require("./profiler");
var tape_1 = require("./tape");
var tensor_1 = require("./tensor");
var util = require("./util");
var Engine = (function () {
function Engine(backend, safeMode) {
this.backend = backend;
this.safeMode = safeMode;
this.registeredVariables = {};
this.refCounter = new WeakMap();
this.nextTapeNodeId = 0;
this.numBytes = 0;
this.numTensors = 0;
this.numDataBuffers = 0;
this.gradientScopeCount = 0;
this.customGradientDepth = 0;
this.activeScope = { keep: [], track: [] };
this.scopeStack = [this.activeScope];
this.profiler = new profiler_1.Profiler(backend);
}
Engine.prototype.runKernel = function (forwardFunc, inputs, backwardsFunc) {
var _this = this;
var result;
var saved = [];
var saveFunc = function (x) {
saved.push(x);
return x;
};
var scopeName = this.activeScope.name;
this.customGradientDepth++;
if (!environment_1.ENV.get('DEBUG')) {
result = forwardFunc(this.backend, saveFunc);
}
else {
result = this.profiler.profileKernel(scopeName, function () { return forwardFunc(_this.backend, saveFunc); });
}
this.customGradientDepth--;
if (this.shouldRecord()) {
var tapeNode = {
id: this.nextTapeNodeId++,
name: scopeName,
inputs: inputs,
output: result,
};
if (backwardsFunc != null) {
tapeNode.gradient = function (dy) { return backwardsFunc(dy, saved); };
}
this.activeTape.push(tapeNode);
}
return result;
};
Engine.prototype.registerTensor = function (a) {
var refCount = this.refCounter.has(a.dataId) ? this.refCounter.get(a.dataId) : 0;
this.numTensors++;
if (refCount === 0) {
this.numDataBuffers++;
this.numBytes +=
util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);
this.backend.register(a.dataId, a.shape, a.dtype);
}
this.refCounter.set(a.dataId, refCount + 1);
if (!(a instanceof tensor_1.Variable)) {
this.track(a);
}
};
Engine.prototype.registerVariable = function (v) {
if (this.registeredVariables[v.name] != null) {
throw new Error("Variable with name " + v.name + " was already registered");
}
this.registeredVariables[v.name] = v;
};
Engine.prototype.disposeTensor = function (a) {
if (!this.refCounter.has(a.dataId)) {
return;
}
this.numTensors--;
var refCount = this.refCounter.get(a.dataId);
if (refCount <= 1) {
this.refCounter.delete(a.dataId);
this.backend.disposeData(a.dataId);
this.numDataBuffers--;
this.numBytes -=
util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);
}
else {
this.refCounter.set(a.dataId, refCount - 1);
}
};
Engine.prototype.disposeVariables = function () {
for (var varName in this.registeredVariables) {
var v = this.registeredVariables[varName];
this.disposeTensor(v);
delete this.registeredVariables[varName];
}
};
Engine.prototype.memory = function () {
var info = this.backend.memory();
info.numTensors = this.numTensors;
info.numDataBuffers = this.numDataBuffers;
info.numBytes = this.numBytes;
return info;
};
Engine.prototype.shouldRecord = function () {
return this.activeTape != null && this.customGradientDepth === 0;
};
Engine.prototype.addTapeNode = function (inputs, result, gradientsFunc) {
var inputsMap = {};
inputs.forEach(function (input, idx) {
inputsMap[idx] = input;
});
var gradient = function (dy) {
var res = gradientsFunc(dy);
var resMap = {};
res.forEach(function (r, idx) {
resMap[idx] = function () { return r; };
});
return resMap;
};
var tapeNode = {
id: this.nextTapeNodeId++,
name: this.activeScope.name,
inputs: inputsMap,
output: result,
gradient: gradient
};
this.activeTape.push(tapeNode);
};
Engine.prototype.keep = function (result) {
if (this.scopeStack.length === 1 && environment_1.ENV.engine.safeMode) {
throw new Error('Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +
'tf.tidy(() => {...}) to avoid memory leaks.');
}
this.activeScope.keep.push(result);
return result;
};
Engine.prototype.startScope = function (name, gradientsMode) {
if (gradientsMode === void 0) { gradientsMode = false; }
if (gradientsMode && this.gradientScopeCount === 0) {
this.activeTape = [];
}
if (gradientsMode) {
this.gradientScopeCount++;
}
var scopeInfo = { keep: [], track: [] };
if (name) {
scopeInfo.name = name;
}
this.scopeStack.push(scopeInfo);
this.activeScope = scopeInfo;
};
Engine.prototype.endScope = function (result, gradientsMode) {
var _this = this;
if (gradientsMode === void 0) { gradientsMode = false; }
if (gradientsMode) {
this.gradientScopeCount--;
if (this.gradientScopeCount === 0) {
this.activeTape = null;
}
}
var tensorsToKeep = this.activeScope.keep;
var tensorsToTrackInParent = util.extractTensorsFromContainer(result);
tensorsToKeep = tensorsToKeep.concat(tensorsToTrackInParent);
for (var i = 0; i < this.activeScope.track.length; i++) {
var tensor = this.activeScope.track[i];
if (util.isTensorInList(tensor, tensorsToKeep)) {
continue;
}
if (this.activeTape != null) {
tensorsToTrackInParent.push(tensor);
}
else {
tensor.dispose();
}
}
this.scopeStack.pop();
this.activeScope = this.scopeStack.length === 0 ?
{ keep: [], track: [] } :
this.scopeStack[this.scopeStack.length - 1];
tensorsToTrackInParent.forEach(function (tensor) {
if (!util.isTensorInList(tensor, _this.activeScope.keep)) {
_this.track(tensor);
}
});
};
Engine.prototype.dispose = function () { };
Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) {
var _this = this;
if (allowNoGradients === void 0) { allowNoGradients = false; }
util.assert(xs.length > 0, 'gradients() received an empty list of xs.');
return globals_1.tidy('gradients', function () {
var y = f();
util.assert(y instanceof tensor_1.Tensor, 'The result y returned by f() must be a tensor.');
var filteredTape = tape_1.getFilteredNodesXToY(_this.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.');
}
var accumulatedGradientMap = {};
accumulatedGradientMap[y.id] = (dy == null) ? ops.ones(y.shape) : dy;
tape_1.backpropagateGradients(accumulatedGradientMap, filteredTape);
var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; });
return { value: y, grads: grads };
}, true);
};
Engine.prototype.customGrad = function (f) {
var _this = this;
util.assert(util.isFunction(f), '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; }), 'The args passed in customGrad(f)(x1, x2,...) must all be tensors');
_this.customGradientDepth++;
var gradientsFunc;
var gradientsMode = true;
var result = globals_1.tidy(f.name, function () {
var _a = f.apply(void 0, inputs), value = _a.value, gradFunc = _a.gradFunc;
util.assert(value instanceof tensor_1.Tensor, 'The function f passed in customGrad(f) must return an object ' +
'where `obj.value` is a tensor');
util.assert(util.isFunction(gradFunc), 'The function f passed in customGrad(f) must return an object ' +
'where `obj.gradFunc` is a function.');
gradientsFunc = gradFunc;
return value;
}, gradientsMode);
_this.customGradientDepth--;
if (_this.shouldRecord()) {
var gradFunc = function (dy) {
var res = gradientsFunc(dy);
var grads = Array.isArray(res) ? res : [res];
util.assert(grads.length === inputs.length, '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; }), 'The function f passed in customGrad(f) must return an object ' +
'where `obj.gradFunc` is a function that returns a list of ' +
'only tensors.');
return grads;
};
_this.addTapeNode(inputs, result, gradFunc);
}
return result;
};
};
Engine.prototype.write = function (dataId, values) {
this.backend.write(dataId, values);
};
Engine.prototype.readSync = function (dataId) {
return this.backend.readSync(dataId);
};
Engine.prototype.read = function (dataId) {
return this.backend.read(dataId);
};
Engine.prototype.fromPixels = function (pixels, numChannels) {
return this.backend.fromPixels(pixels, numChannels);
};
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 = performance.now();
return [4, this.backend.time(query)];
case 1:
timingInfo = _a.sent();
timingInfo.wallMs = performance.now() - start;
return [2, timingInfo];
}
});
});
};
Engine.prototype.track = function (result) {
if (this.scopeStack.length === 1 && this.safeMode) {
throw new Error('Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +
'tf.tidy(() => {op();...}); to avoid memory leaks.');
}
this.activeScope.track.push(result);
return result;
};
return Engine;
}());
exports.Engine = Engine;
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