CUBLAS Sgemm confusing results - cuda

Question

For two matrices X and Q of size 4x3 and 2x3
which in memory look like
x = [0 1 2 3 4 5 6 7 8 9 10 11]
q = [3 4 5 6 7 8]
I tried to use cublas multiplication cublasSgemm, but I couldn't manage to get expected results.
Since they are stored in row-major order so they should be interpreted as 3x4 and 3x2 so it seemed for me that
cublasSgemm(cublas_handle,
CUBLAS_OP_T, CUBLAS_OP_N,
q_rows_num, x_rows_num, dim,
&alpha, // 1
q_device, q_rows_num,
x, x_rows_num,
&beta, // 0
x_q_multiplication, q_rows_num);
where
dim = 3
x_rows_num = 4
q_rows_num = 2
would work but in that case I got error
** On entry to SGEMM parameter number 8 had an illegal value
I also tried shuffling parameters a bit but I couldn't find any setup that would work.
So is it possible to multiply them without changing to column-major order?
EDIT:
So I got exepected results with changes made in this working example:
#include <cublas_v2.h>
#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
int main()
{
int x_rows_num = 4;
int q_rows_num = 2;
int dim = 3;
int N = x_rows_num*dim;
int M = q_rows_num*dim;
float *x, *q, *x_q_multiplication;
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&q, M*sizeof(float));
cudaMallocManaged(&x_q_multiplication, q_rows_num*x_rows_num*dim);
for (int i = 0; i< N; i++) x[i] = i*1.0f;
for (int i = 0; i< M; i++) q[i] = (i + 3)*1.0f;
float *q_device;
cudaMallocManaged(&q_device, M*sizeof(float));
cudaMemcpy(q_device, q, M*sizeof(float), cudaMemcpyHostToDevice);
cublasHandle_t handle;
cublasCreate(&handle);
float alpha = 1.f;
float beta = 0.f;
cublasSgemm(handle,
CUBLAS_OP_T, CUBLAS_OP_N,
x_rows_num, q_rows_num, dim,
&alpha,
x, dim,
q, dim,
&beta,
x_q_multiplication, x_rows_num);
cudaDeviceSynchronize();
for (int i = 0; i < q_rows_num*x_rows_num; i++) std::cout << x_q_multiplication[i] << " ";
cudaFree(x);
cudaFree(q);
cudaFree(x_q_multiplication);
return 0;
}
However I'am still not sure why dim became leading dimension

Answer 1

Your original CUBLAS call:
cublasSgemm(cublas_handle,
CUBLAS_OP_T, CUBLAS_OP_N,
q_rows_num, x_rows_num, dim,
&alpha, // 1
q_device, q_rows_num,
x, x_rows_num,
&beta, // 0
x_q_multiplication, q_rows_num);
was close to correct. Your interpretation of what the leading dimensions should be was correct. What you got wrong was the Op specifiers. If both matrices are row major ordered and the first array needs to be read in its (row major) transposed order, then the operation should be:
#include <cublas_v2.h>
#include <cstring>
#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
int main()
{
int x_rows_num = 4;
int q_rows_num = 2;
int dim = 3;
int N = x_rows_num*dim;
int M = q_rows_num*dim;
float x0[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
float q0[6] = {3, 4, 5, 6, 7, 8 };
float *x, *q, *x_q_multiplication;
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&q, M*sizeof(float));
cudaMallocManaged(&x_q_multiplication, q_rows_num*x_rows_num*dim);
std::memcpy(x, x0, N*sizeof(float));
std::memcpy(q, q0, M*sizeof(float));
float *q_device;
cudaMallocManaged(&q_device, M*sizeof(float));
cudaMemcpy(q_device, q, M*sizeof(float), cudaMemcpyHostToDevice);
cublasHandle_t handle;
cublasCreate(&handle);
float alpha = 1.f;
float beta = 0.f;
cublasSgemm(handle,
CUBLAS_OP_N, CUBLAS_OP_T,
q_rows_num, x_rows_num, dim,
&alpha, // 1
q_device, q_rows_num,
x, x_rows_num,
&beta, // 0
x_q_multiplication, q_rows_num);
cudaDeviceSynchronize();
for (int i = 0; i < q_rows_num*x_rows_num; i++) std::cout << x_q_multiplication[i] << " "; std::cout << std::endl;
cudaFree(x);
cudaFree(q);
cudaFree(x_q_multiplication);
return 0;
}
which does this for me:
$ nvcc -arch=sm_52 cublas_trans.cu -o cublas_trans -lcublas
$ ./cublas_trans
76 88 91 106 106 124 121 142
and which I believe is the correct answer.
Incidentally, Robert Crovella's now deleted comment, which you say you take offense to was 100% correct. I suspect he read, as I did, your original CUBLAS call, interpreted the arguments and concluded, as I did, and as CUBLAS itself did, that you are trying to multiply a 3x4 matrix and a 3x2 matrix. Which is why the invalid argument error was raised.