@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
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 = 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;
if (y = 0, t) op = [op[0] & 2, 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:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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 };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
var backend_webgl_1 = require("./backend_webgl");
jasmine_util_1.describeWithFlags('lazy packing and unpacking', test_util_1.WEBGL_ENVS, function () {
var webglLazilyUnpackFlagSaved;
beforeAll(function () {
webglLazilyUnpackFlagSaved = tf.ENV.get('WEBGL_LAZILY_UNPACK');
tf.ENV.set('WEBGL_LAZILY_UNPACK', true);
});
afterAll(function () {
tf.ENV.set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
});
it('should not leak memory when lazily unpacking', function () {
var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
var c = tf.matMul(a, b);
var startNumBytes = tf.memory().numBytes;
var startNumTensors = tf.memory().numTensors;
tf.add(c, 1);
expect(tf.memory().numBytes - startNumBytes).toEqual(16);
expect(tf.memory().numTensors - startNumTensors).toEqual(1);
});
it('should not leak memory when lazily packing', function () {
var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
var c = tf.add(a, 1);
var startNumBytes = tf.memory().numBytes;
var startNumTensors = tf.memory().numTensors;
tf.matMul(b, c);
expect(tf.memory().numBytes - startNumBytes).toEqual(36);
expect(tf.memory().numTensors - startNumTensors).toEqual(1);
});
it('should work when the same input must be represented by' +
'different textures', function () {
var a = tf.tensor1d([1, 2]);
var res = tf.dot(a, a);
test_util_1.expectArraysClose(res, [5]);
});
});
jasmine_util_1.describeWithFlags('backendWebGL', test_util_1.WEBGL_ENVS, function () {
var prevBackend;
beforeAll(function () {
prevBackend = tf.getBackend();
});
afterEach(function () {
tf.setBackend(prevBackend);
tf.ENV.removeBackend('test-storage');
});
it('register empty string tensor', function () {
var backend = new backend_webgl_1.MathBackendWebGL();
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var t = tf.Tensor.make([3], {}, 'string');
expect(backend.readSync(t.dataId) == null).toBe(true);
});
it('register empty string tensor and write', function () {
var backend = new backend_webgl_1.MathBackendWebGL();
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var t = tf.Tensor.make([3], {}, 'string');
backend.write(t.dataId, ['c', 'a', 'b']);
test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['c', 'a', 'b']);
});
it('register string tensor with values', function () {
var backend = new backend_webgl_1.MathBackendWebGL();
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var t = tf.Tensor.make([3], { values: ['a', 'b', 'c'] }, 'string');
test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['a', 'b', 'c']);
});
it('register string tensor with values and overwrite', function () {
var backend = new backend_webgl_1.MathBackendWebGL();
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var t = tf.Tensor.make([3], { values: ['a', 'b', 'c'] }, 'string');
backend.write(t.dataId, ['c', 'a', 'b']);
test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['c', 'a', 'b']);
});
it('register string tensor with values and wrong shape throws error', function () {
var backend = new backend_webgl_1.MathBackendWebGL();
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
expect(function () { return tf.tensor(['a', 'b', 'c'], [4], 'string'); }).toThrowError();
});
it('delayed storage, reading', function () {
var delayedStorage = true;
var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var texManager = backend.getTextureManager();
var t = tf.Tensor.make([3], {}, 'float32');
backend.write(t.dataId, new Float32Array([1, 2, 3]));
expect(texManager.getNumUsedTextures()).toBe(0);
backend.getTexture(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(1);
test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([1, 2, 3]));
expect(texManager.getNumUsedTextures()).toBe(0);
backend.getTexture(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(1);
backend.disposeData(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(0);
});
it('delayed storage, read packed and then use by an unpacked op', function () {
var delayedStorage = true;
var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var webglPackFlagSaved = tf.ENV.get('WEBGL_PACK');
tf.ENV.set('WEBGL_PACK', true);
var webglSizeUploadUniformSaved = tf.ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM');
tf.ENV.set('WEBGL_SIZE_UPLOAD_UNIFORM', 0);
var a = tf.tensor2d([1, 2], [2, 1]);
var b = tf.tensor2d([1], [1, 1]);
var c = tf.matMul(a, b);
backend.readSync(c.dataId);
tf.ENV.set('WEBGL_PACK', false);
var d = tf.add(c, 1);
tf.ENV.set('WEBGL_PACK', webglPackFlagSaved);
tf.ENV.set('WEBGL_SIZE_UPLOAD_UNIFORM', webglSizeUploadUniformSaved);
test_util_1.expectArraysClose(d, [2, 3]);
});
it('delayed storage, overwriting', function () {
var delayedStorage = true;
var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var texManager = backend.getTextureManager();
var t = tf.Tensor.make([3], {}, 'float32');
backend.write(t.dataId, new Float32Array([1, 2, 3]));
backend.getTexture(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(1);
backend.write(t.dataId, new Float32Array([4, 5, 6]));
expect(texManager.getNumUsedTextures()).toBe(0);
test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([4, 5, 6]));
backend.getTexture(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(1);
test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([4, 5, 6]));
expect(texManager.getNumUsedTextures()).toBe(0);
});
it('immediate storage reading', function () {
var delayedStorage = false;
var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var texManager = backend.getTextureManager();
var t = tf.Tensor.make([3], {}, 'float32');
backend.write(t.dataId, new Float32Array([1, 2, 3]));
expect(texManager.getNumUsedTextures()).toBe(1);
test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([1, 2, 3]));
expect(texManager.getNumUsedTextures()).toBe(1);
backend.disposeData(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(0);
});
it('immediate storage overwriting', function () {
var delayedStorage = false;
var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var texManager = backend.getTextureManager();
var t = tf.Tensor.make([3], {}, 'float32');
backend.write(t.dataId, new Float32Array([1, 2, 3]));
expect(texManager.getNumUsedTextures()).toBe(1);
backend.write(t.dataId, new Float32Array([4, 5, 6]));
expect(texManager.getNumUsedTextures()).toBe(1);
test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([4, 5, 6]));
expect(texManager.getNumUsedTextures()).toBe(1);
backend.disposeData(t.dataId);
expect(texManager.getNumUsedTextures()).toBe(0);
});
it('disposal of backend disposes all textures', function () {
var delayedStorage = false;
var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
var texManager = backend.getTextureManager();
tf.ENV.registerBackend('test-storage', function () { return backend; });
tf.setBackend('test-storage');
var t = tf.Tensor.make([3], {}, 'float32');
backend.write(t.dataId, new Float32Array([1, 2, 3]));
var t2 = tf.Tensor.make([3], {}, 'float32');
backend.write(t2.dataId, new Float32Array([4, 5, 6]));
expect(texManager.getNumUsedTextures()).toBe(2);
backend.dispose();
expect(texManager.getNumUsedTextures()).toBe(0);
});
});
jasmine_util_1.describeWithFlags('Custom window size', test_util_1.WEBGL_ENVS, function () {
it('Set screen area to be 1x1', function () { return __awaiter(_this, void 0, void 0, function () {
var oldBackend, a;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
spyOnProperty(window, 'screen', 'get')
.and.returnValue({ height: 1, width: 1 });
oldBackend = tf.getBackend();
tf.ENV.registerBackend('custom-webgl', function () { return new backend_webgl_1.MathBackendWebGL(); });
tf.setBackend('custom-webgl');
a = tf.ones([100, 100]);
expect(tf.memory().numBytesInGPU).toBe(0);
return [4, a.square().data()];
case 1:
_a.sent();
expect(tf.memory().numBytesInGPU).toBe(0);
test_util_1.expectArraysEqual(a, new Float32Array(100 * 100).fill(1));
tf.setBackend(oldBackend);
tf.ENV.removeBackend('custom-webgl');
return [2];
}
});
}); });
});
var SIZE_UPLOAD_UNIFORM = 4;
var FLOAT32_WEBGL_ENVS = Object.assign({
'WEBGL_RENDER_FLOAT32_ENABLED': true,
'WEBGL_SIZE_UPLOAD_UNIFORM': SIZE_UPLOAD_UNIFORM
}, test_util_1.WEBGL_ENVS);
jasmine_util_1.describeWithFlags('upload tensors as uniforms', FLOAT32_WEBGL_ENVS, function () {
it('small tensor gets uploaded as scalar', function () {
var m = tf.memory();
expect(m.numBytesInGPU).toBe(0);
var a = tf.zeros([SIZE_UPLOAD_UNIFORM - 1]);
a.square();
m = tf.memory();
expect(m.numBytesInGPU).toBe(a.size * 4);
});
it('large tensor gets uploaded to gpu', function () {
var m = tf.memory();
expect(m.numBytesInGPU).toBe(0);
var a = tf.zeros([SIZE_UPLOAD_UNIFORM + 1]);
a.square();
m = tf.memory();
expect(m.numBytesInGPU).toBe(a.size * 4 * 2);
});
it('download and re-upload an output of a shader', function () {
var vals = new Float32Array(SIZE_UPLOAD_UNIFORM + 1);
vals.fill(2);
var a = tf.square(vals);
a.dataSync();
var res = a.square();
var expected = new Float32Array(SIZE_UPLOAD_UNIFORM + 1);
expected.fill(16);
test_util_1.expectArraysClose(res, expected);
});
});
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