mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2025-08-19 12:04:34 +02:00
whisper : fix bench regression
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33
whisper.cpp
33
whisper.cpp
@@ -118,6 +118,21 @@ static void byteswap_tensor(ggml_tensor * tensor) {
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#define WHISPER_USE_SCRATCH
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#define WHISPER_MAX_SCRATCH_BUFFERS 16
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//
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// ggml helpers
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//
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static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
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struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
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if (plan.work_size > 0) {
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buf.resize(plan.work_size);
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plan.work_data = buf.data();
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}
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ggml_graph_compute(graph, &plan);
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}
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// available whisper models
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enum e_model {
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MODEL_UNKNOWN,
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@@ -666,6 +681,7 @@ struct whisper_state {
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// memory buffers used by encode / decode contexts
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std::vector<uint8_t> buf_compute;
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std::vector<uint8_t> buf_work;
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std::vector<uint8_t> buf_scratch[WHISPER_MAX_SCRATCH_BUFFERS];
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int buf_last = 0;
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@@ -1830,8 +1846,8 @@ static bool whisper_encode_internal(
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{
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struct ggml_cgraph gf = {};
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ggml_build_forward_expand (&gf, cur);
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ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
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ggml_build_forward_expand(&gf, cur);
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ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
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//ggml_graph_print(&gf);
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}
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@@ -1916,7 +1932,7 @@ static bool whisper_encode_internal(
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
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}
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ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
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ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
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//ggml_graph_print(&gf);
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}
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@@ -2329,8 +2345,8 @@ static bool whisper_decode_internal(
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// run the computation
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{
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ggml_build_forward_expand (&gf, logits);
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ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
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ggml_build_forward_expand(&gf, logits);
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ggml_graph_compute_helper(wstate.buf_work, &gf, n_threads);
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}
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// extract logits for all N tokens
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@@ -5225,7 +5241,8 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
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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*512);
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std::vector<uint8_t> buf (3llu*N_max*N_max*sizeof(float) + 3*ggml_tensor_overhead());
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std::vector<uint8_t> work(1llu*N_max*N_max*sizeof(float) + 1*ggml_tensor_overhead());
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// put a bunch of random data in the buffer
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for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
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@@ -5280,12 +5297,12 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
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double tsum = 0.0;
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// heat-up
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ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
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ggml_graph_compute_helper(work, &gf, n_threads);
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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_with_ctx(ctx0, &gf, n_threads);
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ggml_graph_compute_helper(work, &gf, n_threads);
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const int64_t t1 = ggml_time_us();
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