Benchmark test case for q4_0 matrix multiplication

Makefile

@@ -256,6 +256,10 @@ embedding: examples/embedding/embedding.cpp ggml.o llama.o common.o
 # Tests
 #
 
+benchmark: ggml.o
+	$(CXX) $(CXXFLAGS) tests/test-benchmark-q4_0-matmult.c ggml.o -o tests/test-benchmark-q4_0-matmult $(LDFLAGS)	
+	tests/test-benchmark-q4_0-matmult
+
 .PHONY: tests
 tests:
 	bash ./tests/run-tests.sh

tests/test-benchmark-q4_0-matmult.c

@@ -0,0 +1,212 @@
+/*
+    License: MIT License
+
+    Changelog:
+    - 2023-03-31 Initial version by Sebastian Apel (https://github.com/SebastianApel)
+
+*/
+
+#include <locale.h>
+#include "ggml.h"
+#include <assert.h>
+#include <math.h>
+#include <cstdio>
+#include <cinttypes>
+#include <unordered_map>
+#include <queue>
+#include <string.h>
+
+uint64_t rdtsc(){
+    unsigned int lo,hi;
+    __asm__ __volatile__ ("rdtsc" : "=a" (lo), "=d" (hi));
+    return ((uint64_t)hi << 32) | lo;
+}
+
+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]; 
+            } 
+        } 
+    }
+    return sum;
+}
+
+
+/*
+    These are mapping to unknown
+    GGML_TYPE_I8,
+    GGML_TYPE_I16,
+    GGML_TYPE_I32,    
+    GGML_TYPE_COUNT,
+*/
+
+#define TENSOR_TYPE_AS_STR(TYPE) TYPE == GGML_TYPE_F32 ? "FP32" : TYPE == GGML_TYPE_F16 ? "FP16" : TYPE == GGML_TYPE_Q4_0 ? "Q4_0" : TYPE == GGML_TYPE_Q4_1 ? "Q4_1" : "UNKNOWN"
+
+#define TENSOR_DUMP(TENSOR) printf("%15s: type = %i (%5s) ne = %5d x %5d x %5d, nb = (%5li, %5li, %5li) - ", #TENSOR, \
+        TENSOR->type,TENSOR_TYPE_AS_STR(TENSOR->type),\
+        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); }
+
+int main(void) {
+    // 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 sizez = 128;
+#else
+    /* Working - let's increase size */
+    const int sizey = 1;
+    const int sizex = (8*32);  
+    const int sizez = 1;
+
+    /*const int sizey = 1;
+    const int sizex = 3*(8*32);  
+    const int sizez = 1;*/
+#endif
+
+    //printf("Memsize required = %i\n", sizex*sizex);
+    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;    
+
+    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,
+        /* no_alloc   =*/ 0
+    };
+
+    ctx = ggml_init(params);
+    if (!ctx) {
+        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=1;
+    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);    
+
+    // 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=1;
+
+    // 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=1;
+    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);
+
+
+    // We cannot use the F32 result, because it will not be exactly the same (due to quantization)
+    // float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]);
+    float sum_of_F32_reference = 11611395072.00f;
+
+    printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_CPU_Cycles; FLOPS_per_Cycle\n");
+    printf("============================================================================================\n");
+
+    setlocale(LC_ALL,"de_DE_UTF8");
+
+    for (int i=0;i<10;i++) {
+
+        long long int start = rdtsc();
+        //printf("Running ggml_graph_compute\n");
+        ggml_graph_compute(ctx, &gf31);
+        long long int stop = rdtsc();
+        long long int cycles = stop-start;
+        float flops_per_cycle = (1.0f*flops_per_matrix)/cycles;
+        printf("%9i;%8i;%6i;%6i;%6i;%15lli;%19lli;%16.2f\n",i,gf31.n_threads, sizex, sizey, sizez, flops_per_matrix, cycles,flops_per_cycle);
+
+#ifdef VERBOSE_DEBUGGING
+        TENSOR_DUMP("res",gf31.nodes[0])
+#endif
+
+        float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]);
+        if (sum_of_Q4_result != sum_of_F32_reference) {
+            printf("\nABORT - ERROR in Matrix Multiplication result - expected %6.2f, got %6.2f\n",
+                sum_of_F32_reference, 
+                sum_of_Q4_result
+            );
+            exit(0);
+        }
+
+        // Running a different graph computation to make sure we override the CPU cache lines    
+        ggml_graph_compute(ctx, &gf32);
+
+    }
+
+}