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bench : fix Windows linkage by moving ggml benches in whisper lib ..

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pull/425/head
Georgi Gerganov 2 years ago
parent 1290fc6457
commit 1ccb8a46a5
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GPG Key ID: 449E073F9DC10735
3 changed files with 151 additions and 135 deletions
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139
examples/bench/bench.cpp
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@ -1,11 +1,8 @@
#include "ggml.h"
#include "whisper.h"
#include <cstdio>
#include <cstring>
#include <string>
#include <thread>
#include <vector>
// command-line parameters
struct whisper_params {
@ -53,7 +50,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, "\n");
}
int bench_whisper_encoder(const whisper_params & params) {
int whisper_bench_encoder(const whisper_params & params) {
// whisper init
struct whisper_context * ctx = whisper_init_from_file(params.model.c_str());
@ -96,132 +93,6 @@ int bench_whisper_encoder(const whisper_params & params) {
return 0;
}
int bench_memcpy(const whisper_params & params) {
size_t n = 50;
size_t arr = params.what > 0 ? 1024 : params.what; // trick to avoid compiler optimizations
// 1 GB array
const size_t size = arr*1024llu*1024llu;
char * src = (char *) malloc(size);
char * dst = (char *) malloc(size);
for (size_t i = 0; i < size; i++) src[i] = i;
memcpy(dst, src, size); // heat-up
double tsum = 0.0;
for (size_t i = 0; i < n; i++) {
const int64_t t0 = ggml_time_us();
memcpy(dst, src, size);
const int64_t t1 = ggml_time_us();
tsum += (t1 - t0)*1e-6;
src[0] = rand();
}
fprintf(stderr, "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
// needed to prevent the compile from optimizing the memcpy away
{
double sum = 0.0;
for (size_t i = 0; i < size; i++) sum += dst[i];
fprintf(stderr, "sum: %s\n", sum == -536870910.00 ? "ok" : "error");
}
free(src);
free(dst);
return 0;
}
int bench_ggml_mul_mat(const whisper_params & params) {
const int n_max = 128;
const std::vector<size_t> sizes = {
64, 128, 256, 512, 1024, 2048, 4096,
};
const size_t N_max = sizes.back();
// a: N*N*sizeof(float)
// b: N*N*sizeof(float)
// c: N*N*sizeof(float)
// when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
std::vector<char> buf(4llu*N_max*N_max*sizeof(float) + 4*256);
for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
for (int j = 0; j < (int) sizes.size(); j++) {
int n_fp16 = 0;
int n_fp32 = 0;
// GFLOPS/s
double s_fp16 = 0.0;
double s_fp32 = 0.0;
const size_t N = sizes[j];
for (int k = 0; k < 2; ++k) {
const ggml_type wtype = k == 0 ? GGML_TYPE_F16 : GGML_TYPE_F32;
double & s = k == 0 ? s_fp16 : s_fp32;
int & n = k == 0 ? n_fp16 : n_fp32;
struct ggml_init_params gparams = {
/*.mem_size =*/ buf.size(),
/*.mem_buffer =*/ buf.data(),
};
struct ggml_context * ctx0 = ggml_init(gparams);
struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype, N, N);
struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);
struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);
struct ggml_cgraph gf = ggml_build_forward(c);
gf.n_threads = params.n_threads;
double tsum = 0.0;
// heat-up
ggml_graph_compute(ctx0, &gf);
for (int i = 0; i < n_max; ++i) {
const int64_t t0 = ggml_time_us();
ggml_graph_compute(ctx0, &gf);
const int64_t t1 = ggml_time_us();
tsum += (t1 - t0)*1e-6;
n++;
if (tsum > 1.0 && n >= 3) {
break;
}
}
ggml_free(ctx0);
s = ((2.0*N*N*N*n)/tsum)*1e-9;
}
fprintf(stderr, "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
N, N, s_fp16, n_fp16, s_fp32, n_fp32);
}
return 0;
}
int main(int argc, char ** argv) {
whisper_params params;
@ -229,14 +100,12 @@ int main(int argc, char ** argv) {
return 1;
}
ggml_time_init();
int ret = -1;
switch (params.what) {
case 0: ret = bench_whisper_encoder(params); break;
case 1: ret = bench_memcpy(params); break;
case 2: ret = bench_ggml_mul_mat(params); break;
case 0: ret = whisper_bench_encoder(params); break;
case 1: ret = whisper_bench_memcpy(params.n_threads); break;
case 2: ret = whisper_bench_ggml_mul_mat(params.n_threads); break;
default: fprintf(stderr, "error: unknown benchmark: %d\n", params.what); break;
}

140
whisper.cpp
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@ -3801,6 +3801,7 @@ int whisper_full(
if (tokens_cur[i].id > whisper_token_beg(ctx) && !params.single_segment) {
const auto t1 = seek + 2*(tokens_cur[i].tid - whisper_token_beg(ctx));
if (!text.empty()) {
const auto tt0 = params.speed_up ? 2*t0 : t0;
const auto tt1 = params.speed_up ? 2*t1 : t1;
@ -4059,6 +4060,145 @@ float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int
// =================================================================================================
//
// Temporary interface needed for exposing ggml interface
// Will be removed in the future when ggml becomes a separate library
//
WHISPER_API int whisper_bench_memcpy(int n_threads) {
ggml_time_init();
size_t n = 50;
size_t arr = n_threads > 0 ? 1024 : n_threads; // trick to avoid compiler optimizations
// 1 GB array
const size_t size = arr*1024llu*1024llu;
char * src = (char *) malloc(size);
char * dst = (char *) malloc(size);
for (size_t i = 0; i < size; i++) src[i] = i;
memcpy(dst, src, size); // heat-up
double tsum = 0.0;
for (size_t i = 0; i < n; i++) {
const int64_t t0 = ggml_time_us();
memcpy(dst, src, size);
const int64_t t1 = ggml_time_us();
tsum += (t1 - t0)*1e-6;
src[0] = rand();
}
fprintf(stderr, "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
// needed to prevent the compile from optimizing the memcpy away
{
double sum = 0.0;
for (size_t i = 0; i < size; i++) sum += dst[i];
fprintf(stderr, "sum: %s\n", sum == -536870910.00 ? "ok" : "error");
}
free(src);
free(dst);
return 0;
}
WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
ggml_time_init();
const int n_max = 128;
const std::vector<size_t> sizes = {
64, 128, 256, 512, 1024, 2048, 4096,
};
const size_t N_max = sizes.back();
// a: N*N*sizeof(float)
// b: N*N*sizeof(float)
// c: N*N*sizeof(float)
// when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
std::vector<char> buf(4llu*N_max*N_max*sizeof(float) + 4*256);
for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
for (int j = 0; j < (int) sizes.size(); j++) {
int n_fp16 = 0;
int n_fp32 = 0;
// GFLOPS/s
double s_fp16 = 0.0;
double s_fp32 = 0.0;
const size_t N = sizes[j];
for (int k = 0; k < 2; ++k) {
const ggml_type wtype = k == 0 ? GGML_TYPE_F16 : GGML_TYPE_F32;
double & s = k == 0 ? s_fp16 : s_fp32;
int & n = k == 0 ? n_fp16 : n_fp32;
struct ggml_init_params gparams = {
/*.mem_size =*/ buf.size(),
/*.mem_buffer =*/ buf.data(),
};
struct ggml_context * ctx0 = ggml_init(gparams);
struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype, N, N);
struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);
struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);
struct ggml_cgraph gf = ggml_build_forward(c);
gf.n_threads = n_threads;
double tsum = 0.0;
// heat-up
ggml_graph_compute(ctx0, &gf);
for (int i = 0; i < n_max; ++i) {
const int64_t t0 = ggml_time_us();
ggml_graph_compute(ctx0, &gf);
const int64_t t1 = ggml_time_us();
tsum += (t1 - t0)*1e-6;
n++;
if (tsum > 1.0 && n >= 3) {
break;
}
}
ggml_free(ctx0);
s = ((2.0*N*N*N*n)/tsum)*1e-9;
}
fprintf(stderr, "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
N, N, s_fp16, n_fp16, s_fp32, n_fp32);
}
return 0;
}
// =================================================================================================
// =================================================================================================
//
// Experimental stuff below
//

7
whisper.h
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@ -350,6 +350,13 @@ extern "C" {
// Get the probability of the specified token in the specified segment.
WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);
////////////////////////////////////////////////////////////////////////////
// Temporary helpers needed for exposing ggml interface
WHISPER_API int whisper_bench_memcpy(int n_threads);
WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads);
#ifdef __cplusplus
}
#endif

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