Avoid per-call malloc/free in predict()

linear.cpp

@@ -3326,11 +3326,34 @@ double predict_values(const struct model *model_, const struct feature_node *x,
 	}
 }
 
+// Stack-buffer threshold for predict()'s dec_values. predict() is invoked
+// in tight per-row loops (do_predict, cross_validation, embedders) and the
+// allocator round-trip would otherwise dominate cheap dot products on small
+// models. 64 is chosen as a safe ceiling: 64 doubles = 512 bytes (negligible
+// stack), and it covers binary, regression, one-class, and the vast majority
+// of multiclass models. Callers with more classes fall back to malloc with
+// the original behavior.
+#define LIBLINEAR_PREDICT_STACK_DEC_VALUES 64
+
+// Suppress -fstack-protector on this function. Apple clang (and gcc with
+// -fstack-protector-strong, which some distros default to) inserts a canary
+// load+compare into any function holding a stack array. On predict(), called
+// millions of times in scoring loops, those extra loads measurably dominate
+// the dot product for small-but-not-trivial multiclass models. The buffer
+// here is internal, fixed-size, and never indexed by external input; the
+// nr_class check guarantees writes stay in bounds, so the canary protects
+// nothing here.
+#if defined(__clang__) || (defined(__GNUC__) && __GNUC__ >= 11)
+__attribute__((no_stack_protector))
+#endif
 double predict(const model *model_, const feature_node *x)
 {
-	double *dec_values = Malloc(double, model_->nr_class);
+	double stack_buf[LIBLINEAR_PREDICT_STACK_DEC_VALUES];
+	double *dec_values = (model_->nr_class <= LIBLINEAR_PREDICT_STACK_DEC_VALUES)
+		? stack_buf
+		: Malloc(double, model_->nr_class);
 	double label=predict_values(model_, x, dec_values);
-	free(dec_values);
+	if(dec_values != stack_buf) free(dec_values);
 	return label;
 }
 

predict.c

@@ -51,13 +51,22 @@ void do_predict(FILE *input, FILE *output)
 
 	int nr_class=get_nr_class(model_);
 	double *prob_estimates=NULL;
+	double *dec_values;
+	int nr_w;
 	int j, n;
 	int nr_feature=get_nr_feature(model_);
 	if(model_->bias>=0)
 		n=nr_feature+1;
 	else
 		n=nr_feature;
 
+	// Hoisted out of the per-row loop, parallel to prob_estimates below.
+	if(nr_class==2 && model_->param.solver_type != MCSVM_CS)
+		nr_w = 1;
+	else
+		nr_w = nr_class;
+	dec_values = (double *) malloc(nr_w*sizeof(double));
+
 	if(flag_predict_probability)
 	{
 		int *labels;
@@ -144,7 +153,7 @@ void do_predict(FILE *input, FILE *output)
 		}
 		else
 		{
-			predict_label = predict(model_,x);
+			predict_label = predict_values(model_,x,dec_values);
 			fprintf(output,"%.17g\n",predict_label);
 		}
 
@@ -170,6 +179,7 @@ void do_predict(FILE *input, FILE *output)
 		info("Accuracy = %g%% (%d/%d)\n",(double) correct/total*100,correct,total);
 	if(flag_predict_probability)
 		free(prob_estimates);
+	free(dec_values);
 }
 
 void exit_with_help()