ExternalVendorCode/whisper.cpp
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whisper : fix bench regression + fix performance when using CPU BLAS (#1275)

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* whisper : fix bench regression

* ggml : use sched_yield when using BLAS + add comment
This commit is contained in:
Georgi Gerganov 2023-09-12 13:54:04 +03:00 committed by GitHub
parent 9b14418863
commit 3fec2119e6
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2 changed files with 36 additions and 11 deletions
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14
ggml.c
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@ -17283,10 +17283,18 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
} else {
// wait for other threads to finish
const int last = node_n;
do {
//sched_yield();
while (true) {
// TODO: this sched_yield can have significant impact on the performance - either positive or negative
// depending on the workload and the operating system.
// since it is not clear what is the best approach, it should potentially become user-configurable
// ref: https://github.com/ggerganov/ggml/issues/291
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
sched_yield();
#endif
node_n = atomic_load(&state->shared->node_n);
} while (node_n == last);
if (node_n != last) break;
};
}
// check if we should stop

33
whisper.cpp
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@ -118,6 +118,21 @@ static void byteswap_tensor(ggml_tensor * tensor) {
#define WHISPER_USE_SCRATCH
#define WHISPER_MAX_SCRATCH_BUFFERS 16
//
// ggml helpers
//
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
// available whisper models
enum e_model {
MODEL_UNKNOWN,
@ -666,6 +681,7 @@ struct whisper_state {
// memory buffers used by encode / decode contexts
std::vector<uint8_t> buf_compute;
std::vector<uint8_t> buf_work;
std::vector<uint8_t> buf_scratch[WHISPER_MAX_SCRATCH_BUFFERS];
int buf_last = 0;
@ -1830,8 +1846,8 @@ static bool whisper_encode_internal(
{
struct ggml_cgraph gf = {};
ggml_build_forward_expand (&gf, cur);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
ggml_build_forward_expand(&gf, cur);
ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
//ggml_graph_print(&gf);
}
@ -1916,7 +1932,7 @@ static bool whisper_encode_internal(
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
}
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
//ggml_graph_print(&gf);
}
@ -2329,8 +2345,8 @@ static bool whisper_decode_internal(
// run the computation
{
ggml_build_forward_expand (&gf, logits);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
ggml_build_forward_expand(&gf, logits);
ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
}
// extract logits for all N tokens
@ -5225,7 +5241,8 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
// 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*512);
std::vector<uint8_t> buf (3llu*N_max*N_max*sizeof(float) + 3*ggml_tensor_overhead());
std::vector<uint8_t> work(1llu*N_max*N_max*sizeof(float) + 1*ggml_tensor_overhead());
// put a bunch of random data in the buffer
for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
@ -5280,12 +5297,12 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
double tsum = 0.0;
// heat-up
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
ggml_graph_compute_helper(work, &gf, n_threads);
for (int i = 0; i < n_max; ++i) {
const int64_t t0 = ggml_time_us();
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
ggml_graph_compute_helper(work, &gf, n_threads);
const int64_t t1 = ggml_time_us();