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benchmark : add tool for timing q4_0 matrix multiplication (#653)
* 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 <ggerganov@gmail.com>
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SebastianApel
GitHub
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2 changed files with 276 additions and 1 deletions
7
Makefile
7
Makefile
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@ -149,7 +149,7 @@ common.o: examples/common.cpp examples/common.h
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$(CXX) $(CXXFLAGS) -c examples/common.cpp -o common.o
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clean:
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rm -vf *.o main quantize quantize-stats perplexity embedding
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rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-q4_0-matmult
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main: examples/main/main.cpp ggml.o llama.o common.o
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$(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o llama.o common.o -o main $(LDFLAGS)
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@ -171,10 +171,15 @@ embedding: examples/embedding/embedding.cpp ggml.o llama.o common.o
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libllama.so: llama.o ggml.o
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$(CXX) $(CXXFLAGS) -shared -fPIC -o libllama.so llama.o ggml.o $(LDFLAGS)
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#
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# Tests
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#
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benchmark: ggml.o
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$(CXX) $(CXXFLAGS) examples/benchmark/benchmark-q4_0-matmult.c ggml.o -o benchmark-q4_0-matmult $(LDFLAGS)
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./benchmark-q4_0-matmult
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.PHONY: tests
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tests:
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bash ./tests/run-tests.sh
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270
examples/benchmark/benchmark-q4_0-matmult.c
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270
examples/benchmark/benchmark-q4_0-matmult.c
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@ -0,0 +1,270 @@
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/*
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License: MIT License
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Changelog:
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- 2023-03-31 Initial version by Sebastian Apel (https://github.com/SebastianApel)
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*/
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#include <locale.h>
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#include "ggml.h"
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#include <assert.h>
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#include <math.h>
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#include <cstring>
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#include <cstdio>
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#include <cinttypes>
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#include <unordered_map>
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#include <queue>
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#include <string.h>
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#include <cassert>
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#include <fstream>
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#include <string>
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#include <iterator>
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#include <algorithm>
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float tensor_sum_elements(struct ggml_tensor * tensor) {
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float sum = 0;
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if (tensor->type==6) {
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for (int j = 0; j < tensor->ne[1]; j++) {
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for (int k = 0; k < tensor->ne[0]; k++) {
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sum += ((float *) tensor->data)[j*tensor->ne[0]+k];
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}
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}
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}
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return sum;
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}
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/*
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These are mapping to unknown
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GGML_TYPE_I8,
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GGML_TYPE_I16,
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GGML_TYPE_I32,
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GGML_TYPE_COUNT,
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*/
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#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"
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#define TENSOR_DUMP(TENSOR) printf("%15s: type = %i (%5s) ne = %5d x %5d x %5d, nb = (%5li, %5li, %5li) - ", #TENSOR, \
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TENSOR->type,TENSOR_TYPE_AS_STR(TENSOR->type),\
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TENSOR->ne[0], TENSOR->ne[1], TENSOR->ne[2], TENSOR->nb[0], TENSOR->nb[1], TENSOR->nb[2]); \
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{ float sum = tensor_sum_elements(TENSOR); printf("Sum of tensor %s is %6.2f\n",#TENSOR, sum); }
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struct benchmark_params_struct {
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int32_t n_threads = 1;
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int32_t n_iterations = 10;
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};
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void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -i N, --iter N number of iterations to use during computation (default: %d)\n", params.n_iterations);
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fprintf(stderr, "\n");
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}
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int main(int argc, char ** argv) {
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struct benchmark_params_struct benchmark_params;
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bool invalid_param = false;
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std::string arg;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg == "-t" || arg == "--threads") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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benchmark_params.n_threads = std::stoi(argv[i]);
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} else if (arg == "-i" || arg == "--iter") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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benchmark_params.n_iterations = std::stoi(argv[i]);
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} else if (arg == "-h" || arg == "--help") {
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print_usage(argc, argv, benchmark_params);
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exit(0);
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}
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if (invalid_param) {
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fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
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print_usage(argc, argv, benchmark_params);
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exit(1);
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}
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}
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// create the ggml context
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printf("Starting Test\n");
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struct ggml_context * ctx;
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//const int sizex = 4096;
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//const int sizey = 11008;
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#undef VERBOSE_DEBUGGING
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#ifndef VERBOSE_DEBUGGING
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const int sizey = 4096;
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const int sizex = 11008;
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const int sizez = 128;
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#else
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/* Working - let's increase size */
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const int sizey = 1;
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const int sizex = (8*32);
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const int sizez = 1;
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/*const int sizey = 1;
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const int sizex = 3*(8*32);
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const int sizez = 1;*/
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#endif
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//printf("Memsize required = %i\n", sizex*sizex);
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ggml_type wtype = GGML_TYPE_F32;
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size_t ctx_size = 0;
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ctx_size += sizex*sizey*ggml_type_sizef(wtype);
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ctx_size += sizex*sizey*ggml_type_sizef(wtype);
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ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
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ctx_size += sizex*sizeof(float);
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ctx_size += 1024*1024*100;
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printf("Allocating Memory of size %li byes, %li MB\n",ctx_size, (ctx_size/1024/1024));
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/* no_alloc =*/ 0
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};
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ctx = ggml_init(params);
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if (!ctx) {
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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return false;
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}
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printf("Creating new tensors\n");
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// printf("Creating new tensor m1\n");
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struct ggml_tensor * m11 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey);
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ggml_set_f32(m11, 1.0f);
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// printf("Creating new tensor m1\n");
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struct ggml_tensor * m12 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey);
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ggml_set_f32(m12, 1.5f);
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// printf("Creating new tensor m2\n");
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struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez);
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ggml_set_f32(m2, 2.0f);
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printf("\n------ Test 1 - Matrix Mult via F32 code ------------------------------------------------------------------------------\n");
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// printf("Creating new tensor m11xm2\n");
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struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2);
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// printf("Creating compute graph\n");
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struct ggml_cgraph gf = ggml_build_forward(m11xm2);
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gf.n_threads=benchmark_params.n_threads;
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printf("cgraph->n_threads=%i\n",gf.n_threads);
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TENSOR_DUMP(m11);
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TENSOR_DUMP(m2);
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ggml_graph_compute(ctx, &gf);
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TENSOR_DUMP(gf.nodes[0]);
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printf("\n------ Test 2 - Matrix Mult via Q4_0 code ------------------------------------------------------------------------------\n");
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int32_t nelements = sizex*sizey;
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int32_t ne[2] = { sizex, sizey };
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std::vector<int64_t> hist_cur(1 << 4, 0);
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// Set up a the benchmark matrices
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// printf("Creating new tensor q11 & Running quantize\n");
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struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey);
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ggml_quantize_q4_0((const float *) m11->data, q11->data, nelements, ne[0], hist_cur.data());
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// Set up a the compute graph
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// printf("Creating new tensor q31\n");
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struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2);
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// printf("Creating compute graph\n");
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struct ggml_cgraph gf31 = ggml_build_forward(q31);
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gf31.n_threads=benchmark_params.n_threads;
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// Set up a second graph computation to make sure we override the CPU cache lines
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// printf("Creating new tensor q12 & Running quantize\n");
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struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, sizex, sizey);
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ggml_quantize_q4_0((const float *) m12->data, q12->data, nelements, ne[0], hist_cur.data());
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// printf("Creating new tensor q32\n");
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struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);
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//printf("Creating compute graph\n");
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struct ggml_cgraph gf32 = ggml_build_forward(q32);
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gf32.n_threads=benchmark_params.n_threads;
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printf("cgraph->n_threads=%i\n",gf31.n_threads);
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const int dimx = sizex;
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const int dimy = sizey;
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const int dimz = sizez;
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long long int flops_per_dot_product = dimy + dimy;
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long long int flops_per_matrix = flops_per_dot_product * dimx * dimz; ;
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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);
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// Let's use the F32 result from above as a reference for the q4_0 multiplication
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float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]);
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printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; FLOPS_per_u_Second\n");
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printf("==============================================================================================\n");
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for (int i=0;i<benchmark_params.n_iterations ;i++) {
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long long int start = ggml_time_us();
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//printf("Running ggml_graph_compute\n");
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ggml_graph_compute(ctx, &gf31);
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long long int stop = ggml_time_us();
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long long int usec = stop-start;
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float sec = usec/1000000;
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float flops_per_usec = (1.0f*flops_per_matrix)/usec;
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printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%19.2f\n",
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i,
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gf31.n_threads,
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sizex, sizey, sizez, flops_per_matrix,
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usec,flops_per_usec);
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#ifdef VERBOSE_DEBUGGING
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TENSOR_DUMP("res",gf31.nodes[0])
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#endif
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// Check that the matrix multiplication result is in the right ballpark
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// We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different
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float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]);
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float delta = abs(sum_of_Q4_result - sum_of_F32_reference);
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float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6
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if (delta > allowed_delta) {
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printf("\nABORT - ERROR in Matrix Multiplication result - expected %6.2f, got %6.2f (delta %6.2f > allowed_delta %6.2f)\n",
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sum_of_F32_reference,
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sum_of_Q4_result,
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delta,
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allowed_delta
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);
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exit(0);
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}
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// Running a different graph computation to make sure we override the CPU cache lines
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ggml_graph_compute(ctx, &gf32);
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}
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}
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