Delete get_Fv_mul_mode method and make related changes since 430188 disabled...

Delete get_Fv_mul_mode method and make related changes since 430188 disabled the Faust-vector mul alternative methods.

src/faust_linear_operator/faust_TransformHelperGen.h

@@ -84,7 +84,6 @@ namespace Faust
 			virtual void copy_mul_mode_state(const TransformHelper<FPP,DEV>& th);
 			void copy_state(const TransformHelper<FPP,DEV>& th);
 			int get_mul_order_opt_mode() const;
-			int get_Fv_mul_mode() const;
 			void eval_sliced_Transform();
 			void eval_fancy_idx_Transform();
 			virtual TransformHelper<FPP, DEV>* slice(faust_unsigned_int start_row_id, faust_unsigned_int end_row_id,
@@ -108,8 +107,6 @@ namespace Faust
 			faust_unsigned_int fancy_num_cols;
 			std::shared_ptr<Transform<FPP,DEV>> transform;
 			int mul_order_opt_mode;
-			int Fv_mul_mode;
-
 	};
 }
 #include "faust_TransformHelperGen.hpp"

src/faust_linear_operator/faust_TransformHelperGen.hpp

@@ -2,7 +2,7 @@
 namespace Faust
 {
 	template<typename FPP, FDevice DEV>
-		TransformHelperGen<FPP,DEV>::TransformHelperGen() : is_transposed(false), is_conjugate(false), is_sliced(false), is_fancy_indexed(false), transform(std::make_shared<Transform<FPP,DEV>>()), mul_order_opt_mode(0), Fv_mul_mode(0)
+		TransformHelperGen<FPP,DEV>::TransformHelperGen() : is_transposed(false), is_conjugate(false), is_sliced(false), is_fancy_indexed(false), transform(std::make_shared<Transform<FPP,DEV>>()), mul_order_opt_mode(0)
 
 	{
 	}
@@ -249,7 +249,6 @@ namespace Faust
 		void TransformHelperGen<FPP,DEV>::copy_mul_mode_state(const TransformHelper<FPP,DEV>& th)
 		{
 			this->mul_order_opt_mode = th.mul_order_opt_mode;
-			this->Fv_mul_mode = th.Fv_mul_mode;
 		}
 
 	template<typename FPP, FDevice DEV>
@@ -258,12 +257,6 @@ namespace Faust
 			return this->mul_order_opt_mode;
 		}
 
-	template<typename FPP, FDevice DEV>
-		int TransformHelperGen<FPP,DEV>::get_Fv_mul_mode() const
-		{
-			return this->Fv_mul_mode;
-		}
-
 	template<typename FPP, FDevice DEV>
 		void TransformHelperGen<FPP, DEV>::eval_sliced_Transform()
 		{

wrapper/matlab/src/mexFaust.cpp.in

@@ -218,9 +218,6 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
 		else if(!strcmp("set_FM_mul_mode", cmd))
 			// def in module faust_optimize
 			set_FM_mul_mode<SCALAR,DEV>(prhs, nrhs, plhs, nlhs);
-		else if(!strcmp("set_Fv_mul_mode", cmd))
-			// def in module faust_optimize
-			set_Fv_mul_mode<SCALAR,DEV>(prhs, nrhs, plhs, nlhs);
 		else if(!strcmp("fourier", cmd))
 			faust_fourier<SCALAR,DEV>(prhs, nrhs, plhs, nlhs);
 		else if(!strcmp("hadamard", cmd))

wrapper/python/pyfaust.py

@@ -3050,16 +3050,16 @@ class FaustMulMode:
 	## \brief The method is basically the same as GREEDY_ALL_BEST_GENMAT but it is implemented using the Torch library.
 	##
 	## This method is only available for the specific packages pyfaust_torch.
-	TORCH_CPU_GREEDY=9
-	## \brief The same as TORCH_CPU_L2R except that torch::chain_matmul is used to
-	## compute in one call the intermediary product of dense contiguous
-	## factors, then the result is multiplied by sparse factors if any remains.
-	##
-	## torch::chain_matmul follows the dynamic programming principle as DYNPROG method does (but the former handles only dense matrices).
-	##
-	## References:
-	## https://pytorch.org/cppdocs/api/function_namespaceat_1aee491a9ff453b6033b4106516bc61a9d.html?highlight=chain_matmul
-	## https://pytorch.org/docs/stable/generated/torch.chain_matmul.html?highlight=chain_matmul#torch.chain_matmul
+    TORCH_CPU_GREEDY=9
+    ## \brief The same as TORCH_CPU_L2R except that torch::chain_matmul is used to
+    ## compute in one call the intermediary product of dense contiguous
+    ## factors, then the result is multiplied by sparse factors if any remains.
+    ##
+    ## torch::chain_matmul follows the dynamic programming principle as DYNPROG method does (but the former handles only dense matrices).
+    ##
+    ## References:
+    ## https://pytorch.org/cppdocs/api/function_namespaceat_1aee491a9ff453b6033b4106516bc61a9d.html?highlight=chain_matmul
+    ## https://pytorch.org/docs/stable/generated/torch.chain_matmul.html?highlight=chain_matmul#torch.chain_matmul
 
-	## This method is only available for the specific packages pyfaust_torch.
-	TORCH_CPU_DENSE_DYNPROG_SPARSE_L2R=10
+    ## This method is only available for the specific packages pyfaust_torch.
+    TORCH_CPU_DENSE_DYNPROG_SPARSE_L2R=10

wrapper/python/src/FaustCoreCppCPU.cpp.in

@@ -28,21 +28,10 @@ void FaustCoreCpp<@TYPE@,Cpu>::multiply(@TYPE@* value_y,int nbrow_y,int nbcol_y,
     }
     if (nbcol_x == 1)
     {
-        if(this->transform->get_Fv_mul_mode() == Faust::DEFAULT_L2R)
-        {
-            // assuming that x and y are pointers to memory allocated to the proper
-            // sizes
-//            Y = this->transform->multiply(value_x);
-            this->transform->multiply(value_x, value_y);
-        }
-        else
-        { // for other ways to multiply, it is not handled yet
-            Faust::Vect<@TYPE@,Cpu> X(nbrow_x,value_x);
-            Faust::Vect<@TYPE@,Cpu> Y;
-
-            Y = this->transform->multiply(X);
-            memcpy(value_y,Y.getData(),sizeof(@TYPE@)*nbrow_y);
-        }
+        // assuming that x and y are pointers to memory allocated to the proper
+        // sizes
+        //            Y = this->transform->multiply(value_x);
+        this->transform->multiply(value_x, value_y);
     }
     else
     {

wrapper/python/src/FaustCoreCppGPU.cpp.in

@@ -29,21 +29,10 @@ void FaustCoreCpp<@TYPE@,GPU2>::multiply(@TYPE@* value_y,int nbrow_y,int nbcol_y
     }
     if (nbcol_x == 1)
     {
-        if(this->transform->get_Fv_mul_mode() == Faust::DEFAULT_L2R)
-        {
-            // assuming that x and y are pointers to memory allocated to the proper
-            // sizes
-//            Y = this->transform->multiply(value_x);
-            this->transform->multiply(value_x, value_y);
-        }
-        else
-        { // for other ways to multiply, it is not handled yet
-            Faust::Vect<@TYPE@,Cpu> X(nbrow_x,value_x);
-            Faust::Vect<@TYPE@,Cpu> Y;
-
-            Y = this->transform->multiply(X);
-            memcpy(value_y,Y.getData(),sizeof(@TYPE@)*nbrow_y);
-        }
+        // assuming that x and y are pointers to memory allocated to the proper
+        // sizes
+        //            Y = this->transform->multiply(value_x);
+        this->transform->multiply(value_x, value_y);
     }
     else
     {