@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
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");
jasmine_util_1.describeWithFlags('kernel_registry', jasmine_util_1.ALL_ENVS, function () {
it('register a kernel and call it', function () {
var called = false;
tf.registerKernel({
kernelName: 'MyKernel',
backendName: tf.getBackend(),
kernelFunc: function (_a) {
var inputs = _a.inputs, attrs = _a.attrs;
expect(attrs.a).toBe(5);
expect(inputs.x.shape).toEqual([2, 2]);
expect(inputs.x.dtype).toBe('float32');
called = true;
return { dtype: 'float32', shape: [3, 3], dataId: {} };
}
});
var inputs = { x: tf.zeros([2, 2]) };
var attrs = { a: 5 };
var res = tf.engine().runKernel('MyKernel', inputs, attrs);
expect(called).toBe(true);
expect(res.dtype).toBe('float32');
expect(res.shape).toEqual([3, 3]);
tf.unregisterKernel('MyKernel', tf.getBackend());
});
it('errors when running non-existent kernel', function () {
var inputs = {};
var attrs = {};
expect(function () { return tf.engine().runKernel('DoesNotExist', inputs, attrs); })
.toThrowError();
});
it('errors when registering the same kernel twice', function () {
tf.registerKernel({
kernelName: 'MyKernel',
backendName: tf.getBackend(),
kernelFunc: function () {
return null;
}
});
expect(function () { return tf.registerKernel({
kernelName: 'MyKernel',
backendName: tf.getBackend(),
kernelFunc: function () {
return null;
}
}); }).toThrowError();
tf.unregisterKernel('MyKernel', tf.getBackend());
});
it('register same kernel on two different backends', function () {
tf.registerBackend('backend1', function () {
return {
id: 1,
dispose: function () { return null; },
disposeData: function (dataId) { return null; },
numDataIds: function () { return 0; }
};
});
tf.registerBackend('backend2', function () {
return {
id: 2,
dispose: function () { return null; },
disposeData: function (dataId) { return null; },
numDataIds: function () { return 0; }
};
});
var lastStorageId = -1;
var kernelFunc = function (_a) {
var backend = _a.backend;
lastStorageId = backend.id;
return { dataId: {}, shape: [], dtype: 'float32' };
};
tf.registerKernel({ kernelName: 'MyKernel', backendName: 'backend1', kernelFunc: kernelFunc });
tf.registerKernel({ kernelName: 'MyKernel', backendName: 'backend2', kernelFunc: kernelFunc });
// No kernel has been executed yet.
expect(lastStorageId).toBe(-1);
// Kernel was executed on the first backend.
tf.setBackend('backend1');
tf.engine().runKernel('MyKernel', {}, {});
expect(lastStorageId).toBe(1);
// Kernel was executed on the second backend.
tf.setBackend('backend2');
tf.engine().runKernel('MyKernel', {}, {});
expect(lastStorageId).toBe(2);
tf.removeBackend('backend1');
tf.removeBackend('backend2');
tf.unregisterKernel('MyKernel', 'backend1');
tf.unregisterKernel('MyKernel', 'backend2');
});
it('register kernel with setup and dispose functions', function () {
var backendName = 'custom-backend';
var kernelName = 'MyKernel';
var customBackend = {
dispose: function () { return null; },
disposeData: function (dataId) { return null; },
numDataIds: function () { return 0; }
};
tf.registerBackend(backendName, function () { return customBackend; });
var kernelFunc = function () {
return { dataId: {}, shape: [], dtype: 'float32' };
};
var setupCalled = false;
var setupFunc = function (backend) {
expect(backend).toBe(customBackend);
setupCalled = true;
};
var disposeCalled = false;
var disposeFunc = function (backend) {
expect(backend).toBe(customBackend);
disposeCalled = true;
};
tf.registerKernel({ kernelName: kernelName, backendName: backendName, kernelFunc: kernelFunc, setupFunc: setupFunc, disposeFunc: disposeFunc });
expect(setupCalled).toBe(false);
expect(disposeCalled).toBe(false);
tf.setBackend(backendName);
expect(setupCalled).toBe(true);
expect(disposeCalled).toBe(false);
// Kernel was executed on the first backend.
tf.engine().runKernel(kernelName, {}, {});
tf.removeBackend(backendName);
expect(setupCalled).toBe(true);
expect(disposeCalled).toBe(true);
tf.unregisterKernel(kernelName, backendName);
});
});
jasmine_util_1.describeWithFlags('gradient registry', jasmine_util_1.ALL_ENVS, function () {
it('register a kernel with gradient and call it', function () { return __awaiter(_this, void 0, void 0, function () {
var kernelWasCalled, gradientWasCalled, kernelName, x, gradFunc, dx, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
kernelWasCalled = false;
gradientWasCalled = false;
kernelName = 'MyKernel';
x = tf.zeros([2, 2]);
tf.registerKernel({
kernelName: kernelName,
backendName: tf.getBackend(),
kernelFunc: function () {
kernelWasCalled = true;
return { dtype: 'float32', shape: [3, 3], dataId: {} };
}
});
tf.registerGradient({
kernelName: kernelName,
gradFunc: function (dy, saved) {
// Make sure saved input (x) was passed to the gradient function.
expect(saved[0].dataId).toEqual(x.dataId);
// Make sure dy matches the shape of the output.
expect(dy.shape).toEqual([3, 3]);
gradientWasCalled = true;
return { x: function () { return tf.fill([2, 2], 3); } };
},
});
gradFunc = tf.grad(function (x) { return tf.engine().runKernel(kernelName, { x: x }, {} /* attrs */, [x] /* inputsToSave */); });
dx = gradFunc(x);
expect(kernelWasCalled).toBe(true);
expect(gradientWasCalled).toBe(true);
expect(dx.dtype).toBe('float32');
expect(dx.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, dx.data()];
case 1:
_a.apply(void 0, [_b.sent(), [3, 3, 3, 3]]);
tf.unregisterKernel(kernelName, tf.getBackend());
tf.unregisterGradient(kernelName);
return [2 /*return*/];
}
});
}); });
it('errors when running non-existent gradient', function () {
var kernelName = 'MyKernel';
var x = tf.zeros([2, 2]);
tf.registerKernel({
kernelName: kernelName,
backendName: tf.getBackend(),
kernelFunc: function () { return ({ dtype: 'float32', shape: [3, 3], dataId: {} }); }
});
var gradFunc = tf.grad(function (x) { return tf.engine().runKernel(kernelName, { x: x }, {} /* attrs */, [x] /* inputsToSave */); });
expect(function () { return gradFunc(x); })
.toThrowError(/gradient function not found for MyKernel/);
tf.unregisterKernel(kernelName, tf.getBackend());
});
it('warning when registering the same gradient twice', function () {
var kernelName = 'MyKernel';
tf.registerGradient({ kernelName: kernelName, gradFunc: function () { return null; } });
spyOn(console, 'warn').and.callFake(function (msg) {
expect(msg).toBe('Overriding the gradient for \'MyKernel\'');
});
tf.registerGradient({ kernelName: kernelName, gradFunc: function () { return null; } });
tf.unregisterGradient(kernelName);
});
});
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