In　real　data　analysis,　practitioners　frequently　come　across　the　case　that　a　discrete　response　will　be　related　to　both　a　function-valued　random　variable　and　a　vector-value　random　variable　as　the　predictor　variables.　In　this　paper,　we　consider　the　generalized　functional　partially　linear　models　(GFPLM).　The　infinite　slope　function　in　the　GFPLM　is　estimated　by　the　principal　component　basis　function　approximations.　Then,　we　consider　the　theoretical　properties　of　the　estimator　obtained　by　maximizing　the　quasi　likelihood　function.　The　asymptotic　normality　of　the　estimator　of　the　finite　dimensional　parameter　and　the　rate　of　convergence　of　the　estimator　of　the　infinite　dimensional　slope　function　are　established,　respectively.　We　investigate　the　finite　sample　properties　of　the　estimation　procedure　via　Monte　Carlo　simulation　studies　and　a　real　data　analysis.