float tensor_sum_elements(struct ggml_tensor * tensor) {
float sum = 0;
- if (tensor->type==6) {
- for (int j = 0; j < tensor->ne[1]; j++) {
- for (int k = 0; k < tensor->ne[0]; k++) {
- sum += ((float *) tensor->data)[j*tensor->ne[0]+k];
- }
- }
+ if (tensor->type==6) {
+ for (int j = 0; j < tensor->ne[1]; j++) {
+ for (int k = 0; k < tensor->ne[0]; k++) {
+ sum += ((float *) tensor->data)[j*tensor->ne[0]+k];
+ }
+ }
}
return sum;
}
These are mapping to unknown
GGML_TYPE_I8,
GGML_TYPE_I16,
- GGML_TYPE_I32,
+ GGML_TYPE_I32,
GGML_TYPE_COUNT,
*/
TENSOR->ne[0], TENSOR->ne[1], TENSOR->ne[2], TENSOR->nb[0], TENSOR->nb[1], TENSOR->nb[2]); \
{ float sum = tensor_sum_elements(TENSOR); printf("Sum of tensor %s is %6.2f\n",#TENSOR, sum); }
-struct benchmark_params_struct {
+struct benchmark_params_struct {
int32_t n_threads = 1;
int32_t n_iterations = 10;
};
int main(int argc, char ** argv) {
-
+
struct benchmark_params_struct benchmark_params;
bool invalid_param = false;
} else if (arg == "-h" || arg == "--help") {
print_usage(argc, argv, benchmark_params);
exit(0);
- }
+ }
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
print_usage(argc, argv, benchmark_params);
// create the ggml context
printf("Starting Test\n");
-
-
+
+
struct ggml_context * ctx;
//const int sizex = 4096;
//const int sizey = 11008;
#undef VERBOSE_DEBUGGING
#ifndef VERBOSE_DEBUGGING
const int sizey = 4096;
- const int sizex = 11008;
+ const int sizex = 11008;
const int sizez = 128;
#else
/* Working - let's increase size */
const int sizey = 1;
- const int sizex = (8*32);
+ const int sizex = (8*32);
const int sizez = 1;
/*const int sizey = 1;
- const int sizex = 3*(8*32);
+ const int sizex = 3*(8*32);
const int sizez = 1;*/
#endif
//printf("Memsize required = %i\n", sizex*sizex);
- ggml_type wtype = GGML_TYPE_F32;
-
+ ggml_type wtype = GGML_TYPE_F32;
+
size_t ctx_size = 0;
ctx_size += sizex*sizey*ggml_type_sizef(wtype);
ctx_size += sizex*sizey*ggml_type_sizef(wtype);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizeof(float);
- ctx_size += 1024*1024*100;
-
+ ctx_size += 1024*1024*100;
+
printf("Allocating Memory of size %li byes, %li MB\n",ctx_size, (ctx_size/1024/1024));
-
+
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
return false;
}
-
-
+
+
printf("Creating new tensors\n");
// printf("Creating new tensor m1\n");
struct ggml_tensor * m11 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey);
ggml_set_f32(m11, 1.0f);
-
+
// printf("Creating new tensor m1\n");
struct ggml_tensor * m12 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey);
ggml_set_f32(m12, 1.5f);
-
+
// printf("Creating new tensor m2\n");
struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez);
ggml_set_f32(m2, 2.0f);
-
+
printf("\n------ Test 1 - Matrix Mult via F32 code ------------------------------------------------------------------------------\n");
// printf("Creating new tensor m11xm2\n");
struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2);
-
+
// printf("Creating compute graph\n");
struct ggml_cgraph gf = ggml_build_forward(m11xm2);
-
+
gf.n_threads=benchmark_params.n_threads;
- printf("cgraph->n_threads=%i\n",gf.n_threads);
-
+ printf("cgraph->n_threads=%i\n",gf.n_threads);
+
TENSOR_DUMP(m11);
TENSOR_DUMP(m2);
-
+
ggml_graph_compute(ctx, &gf);
TENSOR_DUMP(gf.nodes[0]);
-
+
printf("\n------ Test 2 - Matrix Mult via Q4_0 code ------------------------------------------------------------------------------\n");
-
+
int32_t nelements = sizex*sizey;
int32_t ne[2] = { sizex, sizey };
-
- std::vector<int64_t> hist_cur(1 << 4, 0);
+
+ std::vector<int64_t> hist_cur(1 << 4, 0);
// Set up a the benchmark matrices
// printf("Creating new tensor q11 & Running quantize\n");
struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey);
ggml_quantize_q4_0((const float *) m11->data, q11->data, nelements, ne[0], hist_cur.data());
-
+
// Set up a the compute graph
// printf("Creating new tensor q31\n");
struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2);
-
+
// printf("Creating compute graph\n");
struct ggml_cgraph gf31 = ggml_build_forward(q31);
gf31.n_threads=benchmark_params.n_threads;
-
- // Set up a second graph computation to make sure we override the CPU cache lines
+
+ // Set up a second graph computation to make sure we override the CPU cache lines
// printf("Creating new tensor q12 & Running quantize\n");
struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey);
ggml_quantize_q4_0((const float *) m12->data, q12->data, nelements, ne[0], hist_cur.data());
// printf("Creating new tensor q32\n");
struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);
-
+
//printf("Creating compute graph\n");
struct ggml_cgraph gf32 = ggml_build_forward(q32);
gf32.n_threads=benchmark_params.n_threads;
- printf("cgraph->n_threads=%i\n",gf31.n_threads);
-
+ printf("cgraph->n_threads=%i\n",gf31.n_threads);
+
const int dimx = sizex;
const int dimy = sizey;
const int dimz = sizez;
long long int flops_per_dot_product = dimy + dimy;
long long int flops_per_matrix = flops_per_dot_product * dimx * dimz; ;
printf("Matrix Multiplication of (%i,%i,%i) x (%i,%i,%i) - aboout %6.2f gFLOPS\n\n", sizex, sizey, 1, sizex, sizez, 1, 1.0f*flops_per_matrix / 1000 / 1000 / 1000);
-
+
// Let's use the F32 result from above as a reference for the q4_0 multiplication
float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]);
-
+
printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; FLOPS_per_u_Second\n");
printf("==============================================================================================\n");
-
+
for (int i=0;i<benchmark_params.n_iterations ;i++) {
-
+
long long int start = ggml_time_us();
//printf("Running ggml_graph_compute\n");
ggml_graph_compute(ctx, &gf31);
float flops_per_usec = (1.0f*flops_per_matrix)/usec;
printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%19.2f\n",
i,
- gf31.n_threads,
- sizex, sizey, sizez, flops_per_matrix,
+ gf31.n_threads,
+ sizex, sizey, sizez, flops_per_matrix,
usec,flops_per_usec);
#ifdef VERBOSE_DEBUGGING
TENSOR_DUMP("res",gf31.nodes[0])
#endif
- // Check that the matrix multiplication result is in the right ballpark
+ // Check that the matrix multiplication result is in the right ballpark
// We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different
float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]);
float delta = abs(sum_of_Q4_result - sum_of_F32_reference);
if (delta > allowed_delta) {
printf("\nABORT - ERROR in Matrix Multiplication result - expected %6.2f, got %6.2f (delta %6.2f > allowed_delta %6.2f)\n",
- sum_of_F32_reference,
+ sum_of_F32_reference,
sum_of_Q4_result,
delta,
allowed_delta
);
exit(0);
}
-
- // Running a different graph computation to make sure we override the CPU cache lines
+
+ // Running a different graph computation to make sure we override the CPU cache lines
ggml_graph_compute(ctx, &gf32);
-
+
}
-
+
}
overview / pkg / ggml / sources / llama.cpp / commitdiff