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benchmark : add tool for timing q4_0 matrix multiplication (#653)
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* Initial version of q4_0 matrix multiplication benchmark

* Bugfix: Added dependency to ggml.o to benchmark

* Reviewer requests: added parameter for threads, switched to ggml_time_us()

* Reviewer input: removed rtsc, use epsilon for check

* Review comment: Removed set_locale

* Feature: Param for numer of iterations, Bugfix for use of parameter threads

* Reviewer suggestion: Moved to examples

* Reviewer feedback: Updated clean: and benchmark: sections

---------

Co-authored-by: Georgi Gerganov <[email protected]>
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@SebastianApel @ggerganov
SebastianApel and ggerganov authored Apr 13, 2023
1 parent 82d146d commit 95ea26f
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7 changes: 6 additions & 1 deletion Makefile
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Expand Up @@ -149,7 +149,7 @@ common.o: examples/common.cpp examples/common.h
$(CXX) $(CXXFLAGS) -c examples/common.cpp -o common.o

clean:
rm -vf *.o main quantize quantize-stats perplexity embedding
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-q4_0-matmult

main: examples/main/main.cpp ggml.o llama.o common.o
$(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o llama.o common.o -o main $(LDFLAGS)
Expand All @@ -171,10 +171,15 @@ embedding: examples/embedding/embedding.cpp ggml.o llama.o common.o

libllama.so: llama.o ggml.o
$(CXX) $(CXXFLAGS) -shared -fPIC -o libllama.so llama.o ggml.o $(LDFLAGS)

#
# Tests
#

benchmark: ggml.o
$(CXX) $(CXXFLAGS) examples/benchmark/benchmark-q4_0-matmult.c ggml.o -o benchmark-q4_0-matmult $(LDFLAGS)
./benchmark-q4_0-matmult

.PHONY: tests
tests:
bash ./tests/run-tests.sh
270 changes: 270 additions & 0 deletions examples/benchmark/benchmark-q4_0-matmult.c
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@@ -0,0 +1,270 @@
/*
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 <cstring>
#include <cstdio>
#include <cinttypes>
#include <unordered_map>
#include <queue>
#include <string.h>
#include <cassert>
#include <fstream>
#include <string>
#include <iterator>
#include <algorithm>

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); }

struct benchmark_params_struct {
int32_t n_threads = 1;
int32_t n_iterations = 10;
};

void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -i N, --iter N number of iterations to use during computation (default: %d)\n", params.n_iterations);
fprintf(stderr, "\n");
}

int main(int argc, char ** argv) {


struct benchmark_params_struct benchmark_params;

bool invalid_param = false;
std::string arg;
for (int i = 1; i < argc; i++) {
arg = argv[i];

if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
benchmark_params.n_threads = std::stoi(argv[i]);
} else if (arg == "-i" || arg == "--iter") {
if (++i >= argc) {
invalid_param = true;
break;
}
benchmark_params.n_iterations = std::stoi(argv[i]);
} 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);
exit(1);
}
}


// 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=benchmark_params.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);

// 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
// 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);

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);
long long int stop = ggml_time_us();
long long int usec = stop-start;
float sec = usec/1000000;
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,
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
// 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);
float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6

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_Q4_result,
delta,
allowed_delta
);
exit(0);
}

// Running a different graph computation to make sure we override the CPU cache lines
ggml_graph_compute(ctx, &gf32);

}

}

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