In　practice,　it　is　not　uncommon　to　encounter　the　situation　that　a　discrete　response　is　related　to　both　a　functional　random　variable　and　multiple　real-value　random　variables　whose　impact　on　the　response　is　nonlinear.　In　this　paper,　we　consider　the　generalized　partial　functional　linear　additive　models　(GPFLAM)　and　present　the　estimation　procedure.　In　GPFLAM,　the　nonparametric　functions　are　approximated　by　polynomial　splines　and　the　infinite　slope　function　is　estimated　based　on　the　principal　component　basis　function　approximations.　We　obtain　the　estimator　by　maximizing　the　quasi-likelihood　function.　We　investigate　the　finite　sample　properties　of　the　estimation　procedure　via　Monte　Carlo　simulation　studies　and　illustrate　our　proposed　model　by　a　real　data　analysis.