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https://github.com/ggerganov/whisper.cpp.git
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bench : fix Windows linkage by moving ggml benches in whisper lib ..
This commit is contained in:
Georgi Gerganov
parent
1290fc6457
commit
7aa1174315
@ -1,11 +1,8 @@
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#include "ggml.h"
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#include "whisper.h"
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#include <cstdio>
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#include <cstring>
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#include <string>
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#include <thread>
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#include <vector>
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// command-line parameters
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struct whisper_params {
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@ -53,7 +50,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
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fprintf(stderr, "\n");
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}
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int bench_whisper_encoder(const whisper_params & params) {
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int whisper_bench_encoder(const whisper_params & params) {
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// whisper init
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struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
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@ -96,132 +93,6 @@ int bench_whisper_encoder(const whisper_params & params) {
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return 0;
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}
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int bench_memcpy(const whisper_params & params) {
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size_t n = 50;
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size_t arr = params.what > 0 ? 1024 : params.what; // trick to avoid compiler optimizations
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// 1 GB array
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const size_t size = arr*1024llu*1024llu;
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char * src = (char *) malloc(size);
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char * dst = (char *) malloc(size);
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for (size_t i = 0; i < size; i++) src[i] = i;
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memcpy(dst, src, size); // heat-up
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double tsum = 0.0;
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for (size_t i = 0; i < n; i++) {
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const int64_t t0 = ggml_time_us();
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memcpy(dst, src, size);
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const int64_t t1 = ggml_time_us();
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tsum += (t1 - t0)*1e-6;
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src[0] = rand();
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}
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fprintf(stderr, "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
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// needed to prevent the compile from optimizing the memcpy away
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{
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double sum = 0.0;
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for (size_t i = 0; i < size; i++) sum += dst[i];
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fprintf(stderr, "sum: %s\n", sum == -536870910.00 ? "ok" : "error");
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}
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free(src);
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free(dst);
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return 0;
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}
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int bench_ggml_mul_mat(const whisper_params & params) {
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const int n_max = 128;
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const std::vector<size_t> sizes = {
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64, 128, 256, 512, 1024, 2048, 4096,
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};
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const size_t N_max = sizes.back();
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// a: N*N*sizeof(float)
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// b: N*N*sizeof(float)
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// c: N*N*sizeof(float)
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// when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
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std::vector<char> buf(4llu*N_max*N_max*sizeof(float) + 4*256);
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for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
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for (int j = 0; j < (int) sizes.size(); j++) {
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int n_fp16 = 0;
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int n_fp32 = 0;
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// GFLOPS/s
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double s_fp16 = 0.0;
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double s_fp32 = 0.0;
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const size_t N = sizes[j];
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for (int k = 0; k < 2; ++k) {
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const ggml_type wtype = k == 0 ? GGML_TYPE_F16 : GGML_TYPE_F32;
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double & s = k == 0 ? s_fp16 : s_fp32;
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int & n = k == 0 ? n_fp16 : n_fp32;
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struct ggml_init_params gparams = {
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/*.mem_size =*/ buf.size(),
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/*.mem_buffer =*/ buf.data(),
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};
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struct ggml_context * ctx0 = ggml_init(gparams);
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struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype, N, N);
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struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);
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struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);
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struct ggml_cgraph gf = ggml_build_forward(c);
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gf.n_threads = params.n_threads;
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double tsum = 0.0;
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// heat-up
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ggml_graph_compute(ctx0, &gf);
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for (int i = 0; i < n_max; ++i) {
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const int64_t t0 = ggml_time_us();
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ggml_graph_compute(ctx0, &gf);
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const int64_t t1 = ggml_time_us();
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tsum += (t1 - t0)*1e-6;
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n++;
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if (tsum > 1.0 && n >= 3) {
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break;
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}
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}
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ggml_free(ctx0);
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s = ((2.0*N*N*N*n)/tsum)*1e-9;
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}
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fprintf(stderr, "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
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N, N, s_fp16, n_fp16, s_fp32, n_fp32);
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}
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return 0;
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}
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int main(int argc, char ** argv) {
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whisper_params params;
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@ -229,14 +100,12 @@ int main(int argc, char ** argv) {
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return 1;
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}
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ggml_time_init();
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int ret = -1;
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switch (params.what) {
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case 0: ret = bench_whisper_encoder(params); break;
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case 1: ret = bench_memcpy(params); break;
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case 2: ret = bench_ggml_mul_mat(params); break;
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case 0: ret = whisper_bench_encoder(params); break;
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case 1: ret = whisper_bench_memcpy(params.n_threads); break;
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case 2: ret = whisper_bench_ggml_mul_mat(params.n_threads); break;
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default: fprintf(stderr, "error: unknown benchmark: %d\n", params.what); break;
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}
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140
whisper.cpp
140
whisper.cpp
@ -3801,6 +3801,7 @@ int whisper_full(
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if (tokens_cur[i].id > whisper_token_beg(ctx) && !params.single_segment) {
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const auto t1 = seek + 2*(tokens_cur[i].tid - whisper_token_beg(ctx));
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if (!text.empty()) {
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const auto tt0 = params.speed_up ? 2*t0 : t0;
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const auto tt1 = params.speed_up ? 2*t1 : t1;
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@ -4059,6 +4060,145 @@ float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int
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// =================================================================================================
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//
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// Temporary interface needed for exposing ggml interface
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// Will be removed in the future when ggml becomes a separate library
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//
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WHISPER_API int whisper_bench_memcpy(int n_threads) {
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ggml_time_init();
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size_t n = 50;
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size_t arr = n_threads > 0 ? 1024 : n_threads; // trick to avoid compiler optimizations
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// 1 GB array
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const size_t size = arr*1024llu*1024llu;
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char * src = (char *) malloc(size);
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char * dst = (char *) malloc(size);
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for (size_t i = 0; i < size; i++) src[i] = i;
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memcpy(dst, src, size); // heat-up
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double tsum = 0.0;
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for (size_t i = 0; i < n; i++) {
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const int64_t t0 = ggml_time_us();
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memcpy(dst, src, size);
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const int64_t t1 = ggml_time_us();
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tsum += (t1 - t0)*1e-6;
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src[0] = rand();
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}
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fprintf(stderr, "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
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// needed to prevent the compile from optimizing the memcpy away
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{
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double sum = 0.0;
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for (size_t i = 0; i < size; i++) sum += dst[i];
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fprintf(stderr, "sum: %s\n", sum == -536870910.00 ? "ok" : "error");
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}
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free(src);
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free(dst);
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return 0;
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}
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WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
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ggml_time_init();
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const int n_max = 128;
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const std::vector<size_t> sizes = {
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64, 128, 256, 512, 1024, 2048, 4096,
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};
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const size_t N_max = sizes.back();
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// a: N*N*sizeof(float)
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// b: N*N*sizeof(float)
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// c: N*N*sizeof(float)
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// when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
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std::vector<char> buf(4llu*N_max*N_max*sizeof(float) + 4*256);
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for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
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for (int j = 0; j < (int) sizes.size(); j++) {
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int n_fp16 = 0;
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int n_fp32 = 0;
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// GFLOPS/s
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double s_fp16 = 0.0;
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double s_fp32 = 0.0;
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const size_t N = sizes[j];
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for (int k = 0; k < 2; ++k) {
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const ggml_type wtype = k == 0 ? GGML_TYPE_F16 : GGML_TYPE_F32;
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double & s = k == 0 ? s_fp16 : s_fp32;
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int & n = k == 0 ? n_fp16 : n_fp32;
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struct ggml_init_params gparams = {
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/*.mem_size =*/ buf.size(),
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/*.mem_buffer =*/ buf.data(),
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};
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struct ggml_context * ctx0 = ggml_init(gparams);
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struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype, N, N);
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struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);
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struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);
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struct ggml_cgraph gf = ggml_build_forward(c);
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gf.n_threads = n_threads;
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double tsum = 0.0;
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// heat-up
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ggml_graph_compute(ctx0, &gf);
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for (int i = 0; i < n_max; ++i) {
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const int64_t t0 = ggml_time_us();
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ggml_graph_compute(ctx0, &gf);
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const int64_t t1 = ggml_time_us();
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tsum += (t1 - t0)*1e-6;
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n++;
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if (tsum > 1.0 && n >= 3) {
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break;
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}
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}
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ggml_free(ctx0);
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s = ((2.0*N*N*N*n)/tsum)*1e-9;
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}
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fprintf(stderr, "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
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N, N, s_fp16, n_fp16, s_fp32, n_fp32);
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}
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return 0;
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}
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// =================================================================================================
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// =================================================================================================
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//
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// Experimental stuff below
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//
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@ -350,6 +350,13 @@ extern "C" {
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// Get the probability of the specified token in the specified segment.
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WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);
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////////////////////////////////////////////////////////////////////////////
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// Temporary helpers needed for exposing ggml interface
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WHISPER_API int whisper_bench_memcpy(int n_threads);
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WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads);
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#ifdef __cplusplus
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}
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#endif
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