* ggml: aarch64: implement smmla kernel for q8_0_q8_0 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q8_0_q8_0 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: aarch64: implement smmla kernel for q4_0_q8_0 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q4_0_q8_0 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: aarch64: implement smmla kernel for q4_1_q8_1 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q4_1_q8_1 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: update unit tests for the new vec_dot interface * llama.cpp: add MATMUL_INT8 capability to system_info
364 lines
14 KiB
C++
364 lines
14 KiB
C++
// Benchmark quantization specific functions on synthetic data
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#include "ggml.h"
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#undef NDEBUG
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#include <algorithm>
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#include <assert.h>
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#include <functional>
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#include <inttypes.h>
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#include <math.h>
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#include <memory>
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#include <stdio.h>
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#include <string>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#define MAX_ALIGNMENT 64
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#define QK 32
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#define WARMUP 5
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#define ITERATIONS 10
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#define MAX_ITERATIONS 100000000
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#define L1_SIZE 32*128
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#define L2_SIZE 32*2048
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#define L3_SIZE 32*20480
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#define MEM_SIZE 32*2048000
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struct quantize_perf_params {
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std::vector<std::string> include_types;
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std::vector<size_t> test_sizes;
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size_t alignment_offset = 0;
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bool op_quantize_row_q_reference = false;
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bool op_quantize_row_q = false;
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bool op_dequantize_row_q = false;
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bool op_quantize_row_q_dot = false;
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bool op_vec_dot_q = false;
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int64_t iterations = ITERATIONS;
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};
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#if defined(__x86_64__) || defined(__i386__)
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#include <x86intrin.h>
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inline int64_t cpu_cycles() {
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// Rough way to detect new-ish CPUs
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#ifdef __POPCNT__
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unsigned int dummy;
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return __rdtscp(&dummy);
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#else
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return __rdtsc();
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#endif
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}
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#else
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#define cpu_cycles() 0
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#endif
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// Generate synthetic data
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static void generate_data(float offset, size_t n, float * dst) {
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for (size_t i = 0; i < n; i++) {
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dst[i] = 0.1 + 2*cosf(i + offset);
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}
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}
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static float gigabytes_per_second(size_t bytes, int64_t usecs) {
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return bytes / (float) usecs * 1000000 / (1024*1024*1024);
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}
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static void * align_with_offset(void * ptr, int offset) {
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size_t dummy_size = MAX_ALIGNMENT * 4;
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return (char *) std::align(MAX_ALIGNMENT, MAX_ALIGNMENT, ptr, dummy_size) + offset;
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}
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static void benchmark_function(size_t size, size_t q_size, int64_t iterations, const std::function<float(void)> & func) {
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int64_t min_time_us = INT64_MAX;
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int64_t total_time_us = 0;
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int64_t min_time_cycles = INT64_MAX;
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int64_t total_time_cycles = 0;
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for (int i = 0; i < WARMUP; i++) {
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func();
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}
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for (int i = 0; i < iterations; i++) {
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const int64_t start_time = ggml_time_us();
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const int64_t start_cycles = cpu_cycles();
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func();
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const int64_t end_cycles = cpu_cycles();
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const int64_t end_time = ggml_time_us();
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total_time_cycles += end_cycles - start_cycles;
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min_time_cycles = std::min(min_time_cycles, end_cycles - start_cycles);
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total_time_us += end_time - start_time;
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min_time_us = std::min(min_time_us, end_time - start_time);
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}
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printf(" min cycles/%d vals : %9.2f\n", QK, QK * min_time_cycles / (float) size);
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printf(" avg cycles/%d vals : %9.2f\n", QK, QK * total_time_cycles / (float) (size * iterations));
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printf(" float32 throughput : %9.2f GB/s\n", gigabytes_per_second(4 * size * iterations, total_time_us));
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printf(" quantized throughput : %9.2f GB/s\n", gigabytes_per_second(q_size * iterations, total_time_us));
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}
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static void usage(char * argv[]) {
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printf("Benchmark quantization specific functions on synthetic data\n");
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printf("\n");
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printf("usage: %s [options]\n", argv[0]);
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printf("\n");
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printf("options: (default)\n");
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printf(" -h, --help show this help message and exit\n");
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printf(" --size SIZE set test size, divisible by 32 (L1_SIZE:%d)\n", L1_SIZE);
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printf(" -3 use size as L1, L2, L3 sizes (L1:%d L2:%d L3:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE);
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printf(" -4 use size as L1, L2, L3, MEM sizes (L1:%d L2:%d L3:%d MEM:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE, MEM_SIZE);
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printf(" --op OP set test operation as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
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printf(" quantize_row_q_dot, vec_dot_q (all)\n");
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printf(" --type TYPE set test type as");
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for (int i = 0; i < GGML_TYPE_COUNT; i++) {
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ggml_type type = (ggml_type) i;
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ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
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if (ggml_type_name(type) != NULL) {
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if (qfns.from_float && qfns.to_float) {
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printf(" %s", ggml_type_name(type));
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}
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}
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}
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printf(" (all)\n");
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printf(" --alignment-offset OFFSET\n");
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printf(" set alignment offset as OFFSET (0)\n");
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printf(" -i NUM, --iterations NUM\n");
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printf(" set test iteration number (%d)\n", ITERATIONS);
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}
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int main(int argc, char * argv[]) {
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quantize_perf_params params {};
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// read command line
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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 == "--size") {
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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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size_t size = std::stoi(argv[i]);
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if (size % 32 != 0) {
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fprintf(stderr, "error: size %zu not divisible by 32\n", size);
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invalid_param = true;
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break;
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}
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params.test_sizes.push_back(size);
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} else if (arg == "-3") {
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// quick select sizes that probably fit in CPU caches
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params.test_sizes.push_back(L1_SIZE);
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params.test_sizes.push_back(L2_SIZE);
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params.test_sizes.push_back(L3_SIZE);
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} else if (arg == "-4") {
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// quick select cache sizes + memory
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params.test_sizes.push_back(L1_SIZE);
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params.test_sizes.push_back(L2_SIZE);
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params.test_sizes.push_back(L3_SIZE);
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params.test_sizes.push_back(MEM_SIZE);
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} else if (arg == "--op") {
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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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std::string op {argv[i]};
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if (op == "quantize_row_q_reference") {
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params.op_quantize_row_q_reference = true;
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} else if (op == "quantize_row_q") {
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params.op_quantize_row_q = true;
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} else if (op == "dequantize_row_q") {
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params.op_dequantize_row_q = true;
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} else if (op == "quantize_row_q_dot") {
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params.op_quantize_row_q_dot = true;
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} else if (op == "vec_dot_q") {
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params.op_vec_dot_q = true;
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} else {
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invalid_param = true;
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break;
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}
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} else if (arg == "--type") {
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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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params.include_types.push_back(argv[i]);
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} else if (arg == "--alignment-offset") {
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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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int alignment = std::stoi(argv[i]);
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if (alignment < 0 || alignment > MAX_ALIGNMENT) {
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fprintf(stderr, "error: alignment-offset must be less than %d\n", MAX_ALIGNMENT);
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invalid_param = true;
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break;
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}
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params.alignment_offset = alignment;
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} else if ((arg == "-i") || (arg == "--iterations")) {
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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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int number = std::stoi(argv[i]);
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if (number < 0 || number > MAX_ITERATIONS) {
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fprintf(stderr, "error: iterations must be less than %d\n", MAX_ITERATIONS);
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invalid_param = true;
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break;
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}
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params.iterations = number;
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} else if ((arg == "-h") || (arg == "--help")) {
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usage(argv);
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return 1;
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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return 1;
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}
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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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return 1;
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}
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if (params.test_sizes.empty()) {
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params.test_sizes.push_back(L1_SIZE);
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}
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if (!(params.op_quantize_row_q_reference || params.op_quantize_row_q || params.op_dequantize_row_q || params.op_quantize_row_q_dot || params.op_vec_dot_q)) {
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params.op_quantize_row_q_reference = params.op_quantize_row_q = params.op_dequantize_row_q = params.op_quantize_row_q_dot = params.op_vec_dot_q = true;
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}
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std::sort(params.test_sizes.begin(), params.test_sizes.end());
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size_t largest = params.test_sizes.back();
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std::vector<uint8_t> test_data1_v(largest*4 + MAX_ALIGNMENT*2);
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std::vector<uint8_t> test_data2_v(largest*4 + MAX_ALIGNMENT*2);
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std::vector<uint8_t> test_q1_v (largest*4 + MAX_ALIGNMENT*2);
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std::vector<uint8_t> test_q2_v (largest*4 + MAX_ALIGNMENT*2);
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std::vector<uint8_t> test_out_v (largest*4 + MAX_ALIGNMENT*2);
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float * test_data1 = (float *) align_with_offset(test_data1_v.data(), params.alignment_offset);
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float * test_data2 = (float *) align_with_offset(test_data2_v.data(), params.alignment_offset);
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float * test_q1 = (float *) align_with_offset(test_q1_v.data(), params.alignment_offset);
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float * test_q2 = (float *) align_with_offset(test_q2_v.data(), params.alignment_offset);
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float * test_out = (float *) align_with_offset(test_out_v.data(), params.alignment_offset);
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generate_data(0, largest, test_data1);
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generate_data(1, largest, test_data2);
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int64_t iterations = params.iterations;
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// Initialize GGML, ensures float conversion tables are initialized
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struct ggml_init_params ggml_params = {
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/* .mem_size = */ 1*1024,
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/* .mem_buffer = */ NULL,
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/* .no_alloc = */ true,
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};
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struct ggml_context * ctx = ggml_init(ggml_params);
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for (int i = 0; i < GGML_TYPE_COUNT; i++) {
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ggml_type type = (ggml_type) i;
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ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
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if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
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continue;
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}
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if (qfns.from_float && qfns.to_float) {
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printf("%s\n", ggml_type_name(type));
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ggml_quantize_init(type);
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if (params.op_quantize_row_q_reference) {
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printf(" quantize_row_q_reference\n");
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for (size_t size : params.test_sizes) {
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printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
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auto quantize_fn = [&](void) -> float {
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qfns.from_float_reference(test_data1, test_q1, size);
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return test_q1[0];
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};
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size_t quantized_size = ggml_row_size(type, size);
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benchmark_function(size, quantized_size, iterations, quantize_fn);
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}
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printf("\n");
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}
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if (params.op_quantize_row_q) {
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printf(" quantize_row_q\n");
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for (size_t size : params.test_sizes) {
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printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
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auto quantize_fn = [&](void) -> float {
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qfns.from_float(test_data1, test_q1, size);
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return test_q1[0];
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};
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size_t quantized_size = ggml_row_size(type, size);
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benchmark_function(size, quantized_size, iterations, quantize_fn);
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}
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printf("\n");
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}
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if (params.op_dequantize_row_q) {
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printf(" dequantize_row_q\n");
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qfns.from_float(test_data1, test_q1, largest);
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for (size_t size : params.test_sizes) {
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printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
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auto quantize_fn = [&](void) -> float {
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qfns.to_float(test_q1, test_out, size);
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return test_out[0];
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};
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size_t quantized_size = ggml_row_size(type, size);
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benchmark_function(size, quantized_size, iterations, quantize_fn);
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}
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printf("\n");
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}
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if (params.op_quantize_row_q_dot) {
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printf(" quantize_row_q_dot\n");
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for (size_t size : params.test_sizes) {
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printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
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auto quantize_fn = [&](void) -> float {
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auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
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vdot.from_float(test_data1, test_q1, size);
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return test_q1[0];
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};
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size_t quantized_size = ggml_row_size(type, size);
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benchmark_function(size, quantized_size, iterations, quantize_fn);
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}
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printf("\n");
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}
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if (params.op_vec_dot_q) {
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printf(" vec_dot_q\n");
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qfns.from_float(test_data1, test_q1, largest);
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qfns.from_float(test_data2, test_q2, largest);
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for (size_t size : params.test_sizes) {
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printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
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auto quantize_fn = [&](void) -> float {
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float result;
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qfns.vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1);
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return result;
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};
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size_t quantized_size = ggml_row_size(type, size);
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benchmark_function(size, quantized_size, iterations, quantize_fn);
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}
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printf("\n");
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}
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}
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}
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ggml_free(ctx);
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return 0;
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}
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