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examples : fix benchmark-matmult (#1554)

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The precision for Q4_0 has degraded since #1508
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Georgi Gerganov 2023-09-20 10:02:39 +03:00 committed by GitHub
parent 8781013ef6
commit d119c04c15
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1 changed files with 15 additions and 13 deletions
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examples/benchmark/benchmark-matmult.cpp
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@ -33,7 +33,7 @@ void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph,
} }
float tensor_sum_elements(const ggml_tensor * tensor) { float tensor_sum_elements(const ggml_tensor * tensor) {
float sum = 0; double sum = 0;
if (tensor->type == GGML_TYPE_F32) { if (tensor->type == GGML_TYPE_F32) {
for (int j = 0; j < tensor->ne[1]; j++) { for (int j = 0; j < tensor->ne[1]; j++) {
for (int k = 0; k < tensor->ne[0]; k++) { for (int k = 0; k < tensor->ne[0]; k++) {
@ -126,12 +126,15 @@ int main(int argc, char ** argv) {
//printf("Memsize required = %i\n", sizex*sizex); //printf("Memsize required = %i\n", sizex*sizex);
// TODO: perform the bench for all types or for a user specified type
const ggml_type qtype = GGML_TYPE_Q4_1;
size_t ctx_size = 0; size_t ctx_size = 0;
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32); ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_Q4_0); ctx_size += sizex*sizey*ggml_type_sizef(qtype);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_Q4_0); ctx_size += sizex*sizey*ggml_type_sizef(qtype);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += 1024*1024*16; ctx_size += 1024*1024*16;
@ -164,7 +167,7 @@ int main(int argc, char ** argv) {
struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez); struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez);
ggml_set_f32(m2, 2.0f); ggml_set_f32(m2, 2.0f);
printf("\n------ Test 1 - Matrix Mult via F32 code ------------------------------------------------------------------------------\n"); printf("\n------ Test 1 - Matrix Mult via F32 code\n");
// printf("Creating new tensor m11xm2\n"); // printf("Creating new tensor m11xm2\n");
struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2); struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2);
@ -182,17 +185,16 @@ int main(int argc, char ** argv) {
TENSOR_DUMP(gf.nodes[0]); TENSOR_DUMP(gf.nodes[0]);
printf("\n------ Test 2 - Matrix Mult via Q4_0 code ------------------------------------------------------------------------------\n"); printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype));
int32_t nelements = sizex*sizey; 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 // Set up a the benchmark matrices
// printf("Creating new tensor q11 & Running quantize\n"); // printf("Creating new tensor q11 & Running quantize\n");
struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey); struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
ggml_quantize_q4_0((const float *) m11->data, q11->data, nelements, ne[0], hist_cur.data()); ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements, hist_cur.data());
// Set up a the compute graph // Set up a the compute graph
// printf("Creating new tensor q31\n"); // printf("Creating new tensor q31\n");
@ -203,8 +205,8 @@ int main(int argc, char ** argv) {
// 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"); // printf("Creating new tensor q12 & Running quantize\n");
struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey); struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
ggml_quantize_q4_0((const float *) m12->data, q12->data, nelements, ne[0], hist_cur.data()); ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements, hist_cur.data());
// printf("Creating new tensor q32\n"); // printf("Creating new tensor q32\n");
struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);
@ -221,7 +223,7 @@ int main(int argc, char ** argv) {
printf("Matrix Multiplication of (%i,%i,%i) x (%i,%i,%i) - about %6.2f gFLOPS\n\n", sizex, sizey, 1, sizex, sizez, 1, 1.0f*flops_per_matrix / 1000 / 1000 / 1000); printf("Matrix Multiplication of (%i,%i,%i) x (%i,%i,%i) - about %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 // Let's use the F32 result from above as a reference for the quantized multiplication
float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]); float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]);
printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n"); printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");