test_optimizer.py

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# to you under the Apache License, Version 2.0 (the
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#
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# =============================================================================
from __future__ import division
from builtins import zip
from builtins import range

import unittest
import math
import numpy as np

import singa.tensor as tensor
import singa.optimizer as opt
from singa import singa_wrap

from cuda_helper import gpu_dev as cuda


def np_adam(plist, glist, mlist, vlist, lr, t, b1=0.9, b2=0.999):
    for p, g, m, v in zip(plist, glist, mlist, vlist):
        m *= b1
        m += (1 - b1) * g
        v *= b2
        v += (1 - b2) * g * g
        alpha = lr * math.sqrt(1. - math.pow(b2, t)) / (1. - math.pow(b1, t))
        p -= alpha * m / (np.sqrt(v) + 1e-8)


def np_rmsprop(plist, glist, vlist, lr, t, rho=0.9):
    for p, g, v in zip(plist, glist, vlist):
        v *= rho
        v += (1 - rho) * g * g
        p -= lr * g / (np.sqrt(v + 1e-8))


def np_momentum(plist, glist, vlist, lr, t, momentum=0.9):
    for p, g, v in zip(plist, glist, vlist):
        v *= momentum
        v += lr * g
        p -= v


def np_adagrad(plist, glist, vlist, lr, t):
    for p, g, v in zip(plist, glist, vlist):
        v += g * g
        p -= lr * g / (np.sqrt(v + 1e-8))


class TestOptimizer(unittest.TestCase):

    def setUp(self):
        self.np_W = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32)
        self.W = tensor.from_numpy(self.np_W)
        self.np_g = np.array([0.1, 0.3, 0.1, 0.2], dtype=np.float32)
        self.g = tensor.from_numpy(self.np_g)

    def to_cuda(self):
        self.W.to_device(cuda)
        self.g.to_device(cuda)

    def test_sgd(self):
        lr = 0.1
        sgd = opt.SGD(lr)
        sgd.apply(0, self.g, self.W, 'w')
        w = tensor.to_numpy(self.W)
        for i in range(self.W.size()):
            self.assertAlmostEqual(w[i], self.np_W[i] - lr * self.np_g[i])

    def test_adam(self):
        lr = 0.1
        n, m = 4, 6
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        m1 = np.zeros((n, m))
        m2 = np.zeros((n, m))
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 10):
            np_adam([p1, p2], [g1, g2], [m1, m2], [v1, v2], lr, t)

        adam = opt.Adam(lr=lr)
        for t in range(1, 10):
            adam.apply(0, tg1, t1, 'p1', t)
            adam.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 6)

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_sgd_cuda(self):
        lr = 0.1
        sgd = opt.SGD(lr)
        self.to_cuda()
        sgd.apply(0, self.g, self.W, 'w')
        self.W.to_host()
        w = tensor.to_numpy(self.W)
        for i in range(self.W.size()):
            self.assertAlmostEqual(w[i], self.np_W[i] - lr * self.np_g[i])

    def test_constraint(self):
        threshold = 0.02
        cons = opt.L2Constraint(threshold)
        cons.apply(0, self.W, self.g)
        g = tensor.to_numpy(self.g)
        nrm = np.linalg.norm(self.np_g) / self.np_g.size
        for i in range(g.size):
            self.assertAlmostEqual(g[i], self.np_g[i] * threshold / nrm)

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_constraint_cuda(self):
        threshold = 0.02
        self.to_cuda()
        cons = opt.L2Constraint(threshold)
        cons.apply(0, self.W, self.g)
        self.g.to_host()
        g = tensor.to_numpy(self.g)
        nrm = np.linalg.norm(self.np_g) / self.np_g.size
        for i in range(g.size):
            self.assertAlmostEqual(g[i], self.np_g[i] * threshold / nrm)

    def test_regularizer(self):
        coefficient = 0.0001
        reg = opt.L2Regularizer(coefficient)
        reg.apply(0, self.W, self.g)
        g = tensor.to_numpy(self.g)
        for i in range(g.size):
            self.assertAlmostEqual(g[i],
                                   self.np_g[i] + coefficient * self.np_W[i])

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_regularizer_cuda(self):
        coefficient = 0.0001
        reg = opt.L2Regularizer(coefficient)
        self.to_cuda()
        reg.apply(0, self.W, self.g)
        self.g.to_host()
        g = tensor.to_numpy(self.g)
        for i in range(g.size):
            self.assertAlmostEqual(g[i],
                                   self.np_g[i] + coefficient * self.np_W[i])

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_adam_cuda(self):
        lr = 0.1
        n, m = 4, 6
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        m1 = np.zeros((n, m))
        m2 = np.zeros((n, m))
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 10):
            np_adam([p1, p2], [g1, g2], [m1, m2], [v1, v2], lr, t)

        adam = opt.Adam(lr=lr)
        self.to_cuda()
        for t in range(1, 10):
            adam.apply(0, tg1, t1, 'p1', t)
            adam.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 6)

    def test_rmsprop(self):
        lr = 0.1
        n, m = 2, 2
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 4):
            np_rmsprop([p1, p2], [g1, g2], [v1, v2], lr, t)

        rsmprop = opt.RMSProp(lr=lr)
        for t in range(1, 4):
            rsmprop.apply(0, tg1, t1, 'p1', t)
            rsmprop.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 2)

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_rmsprop_cuda(self):
        lr = 0.1
        n, m = 2, 2
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 4):
            np_rmsprop([p1, p2], [g1, g2], [v1, v2], lr, t)

        rsmprop = opt.RMSProp(lr=lr)
        self.to_cuda()
        for t in range(1, 4):
            rsmprop.apply(0, tg1, t1, 'p1', t)
            rsmprop.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 2)

    def test_momentum(self):
        lr = 0.1
        n, m = 2, 2
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 4):
            np_momentum([p1, p2], [g1, g2], [v1, v2], lr, t)

        momentum = opt.SGD(lr, momentum=0.9)
        for t in range(1, 4):
            momentum.apply(0, tg1, t1, 'p1', t)
            momentum.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 2)

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_momentum_cuda(self):
        lr = 0.1
        n, m = 2, 2
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 4):
            np_momentum([p1, p2], [g1, g2], [v1, v2], lr, t)

        momentum = opt.SGD(lr, momentum=0.9)
        self.to_cuda()
        for t in range(1, 4):
            momentum.apply(0, tg1, t1, 'p1', t)
            momentum.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 2)

    def test_adagrad(self):
        lr = 0.1
        n, m = 2, 2
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 4):
            np_adagrad([p1, p2], [g1, g2], [v1, v2], lr, t)

        adagrad = opt.AdaGrad(lr=lr)
        for t in range(1, 4):
            adagrad.apply(0, tg1, t1, 'p1', t)
            adagrad.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 2)

    @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
    def test_adagrad_cuda(self):
        lr = 0.1
        n, m = 2, 2
        p1 = np.random.rand(n, m)
        p2 = np.random.rand(n, m)
        g1 = np.random.rand(n, m) * 0.01
        g2 = np.random.rand(n, m) * 0.01
        v1 = np.zeros((n, m))
        v2 = np.zeros((n, m))
        t1 = tensor.from_numpy(p1)
        t2 = tensor.from_numpy(p2)
        tg1 = tensor.from_numpy(g1)
        tg2 = tensor.from_numpy(g2)

        for t in range(1, 4):
            np_adagrad([p1, p2], [g1, g2], [v1, v2], lr, t)

        adagrad = opt.AdaGrad(lr=lr)
        self.to_cuda()
        for t in range(1, 4):
            adagrad.apply(0, tg1, t1, 'p1', t)
            adagrad.apply(0, tg2, t2, 'p2', t)

        t1 = tensor.to_numpy(t1)
        t2 = tensor.to_numpy(t2)
        for t, p in zip([t1, t2], [p1, p2]):
            for i in range(n):
                for j in range(m):
                    self.assertAlmostEqual(t[i, j], p[i, j], 2)


if __name__ == '__main__':
    unittest.main()