extern/whisper.cpp
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examples : fix build + compile warnings (close #1256)

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Georgi Gerganov 2023-09-07 12:33:12 +03:00
parent aed5d40607
commit 2818de21ff
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GPG Key ID: 449E073F9DC10735
7 changed files with 29 additions and 24 deletions
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2
examples/common.cpp
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@ -792,7 +792,7 @@ bool sam_params_parse(int argc, char ** argv, sam_params & params) {
return true;
}
void sam_print_usage(int argc, char ** argv, const sam_params & params) {
void sam_print_usage(int /*argc*/, char ** argv, const sam_params & params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");

6
examples/lsp/lsp.cpp
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@ -324,12 +324,12 @@ json register_commandset(struct whisper_context * ctx, json jparams, std::vector
commandset_list.push_back(cs);
return json{{"index",index}};
}
json seek(struct whisper_context * ctx, audio_async &audio, json params) {
json seek(struct whisper_context * /*ctx*/, audio_async & /*audio*/, json /*params*/) {
// whisper_state has the pertinent offsets, but there also seem to be a large
// number of scratch buffers that would prevent rewinding context in a manner similar to llama
// I'll give this a another pass once everything else is implemented,
// but for now, it's unsupported
throw json{
throw json {
{"code", -32601},
{"message", "Seeking is not yet supported."}
};
@ -412,7 +412,7 @@ void process_loop(struct whisper_context * ctx, audio_async &audio, const whispe
jobqueue.pop_front();
// send response
std::string data = resp.dump(-1, ' ', false, json::error_handler_t::replace);
fprintf(stdout, "Content-Length: %d\r\n\r\n%s\n", data.length()+1, data.c_str());
fprintf(stdout, "Content-Length: %d\r\n\r\n%s\n", (int)data.length()+1, data.c_str());
std::cout.flush();
}

4
examples/main/main.cpp
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@ -260,7 +260,7 @@ std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s
return speaker;
}
void whisper_print_progress_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, int progress, void * user_data) {
void whisper_print_progress_callback(struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, int progress, void * user_data) {
int progress_step = ((whisper_print_user_data *) user_data)->params->progress_step;
int * progress_prev = &(((whisper_print_user_data *) user_data)->progress_prev);
if (progress >= *progress_prev + progress_step) {
@ -492,7 +492,7 @@ bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_
return true;
}
bool output_score(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
bool output_score(struct whisper_context * ctx, const char * fname, const whisper_params & /*params*/, std::vector<std::vector<float>> /*pcmf32s*/) {
std::ofstream fout(fname);
fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);

25
examples/talk-llama/llama.cpp
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@ -1164,7 +1164,7 @@ static bool llama_eval_internal(
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads) {
int n_threads) {
// enforce that the first token is BOS
if (n_past == 0 && tokens[0] != llama_token_bos()) {
@ -1190,6 +1190,8 @@ static bool llama_eval_internal(
const int n_vocab = hparams.n_vocab;
const int n_rot = hparams.n_embd/hparams.n_head;
const float eps = 5e-6f; // TODO: take from hparams
auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
@ -1204,7 +1206,7 @@ static bool llama_eval_internal(
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
ggml_cgraph gf = {};
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
@ -1221,7 +1223,7 @@ static bool llama_eval_internal(
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
cur = ggml_rms_norm(ctx0, inpL, eps);
// cur = cur*attention_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
@ -1329,7 +1331,7 @@ static bool llama_eval_internal(
{
// norm
{
cur = ggml_rms_norm(ctx0, inpFF);
cur = ggml_rms_norm(ctx0, inpFF, eps);
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
@ -1367,7 +1369,7 @@ static bool llama_eval_internal(
// norm
{
inpL = ggml_rms_norm(ctx0, inpL);
inpL = ggml_rms_norm(ctx0, inpL, eps);
// inpL = inpL*norm(broadcasted)
inpL = ggml_mul(ctx0, inpL, model.norm);
@ -1384,8 +1386,8 @@ static bool llama_eval_internal(
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_build_forward_expand (&gf, inpL);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
@ -2488,8 +2490,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
}
struct ggml_cgraph gf = ggml_build_forward(r);
gf.n_threads = n_threads;
ggml_graph_compute(lora_ctx, &gf);
ggml_graph_compute_with_ctx(lora_ctx, &gf, n_threads);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);
@ -2635,7 +2636,6 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kout3d->data = out;
@ -2655,7 +2655,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_graph_compute_with_ctx(cpy_ctx, &gf, 1);
ggml_free(cpy_ctx);
}
@ -2743,7 +2743,6 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kin3d->data = (void *) inp;
@ -2763,7 +2762,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_graph_compute_with_ctx(cpy_ctx, &gf, 1);
ggml_free(cpy_ctx);
}

5
examples/talk-llama/talk-llama.cpp
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@ -649,7 +649,10 @@ int main(int argc, char ** argv) {
}
text_to_speak = ::replace(text_to_speak, "\"", "");
system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
int ret = system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
if (ret != 0) {
fprintf(stderr, "%s: failed to speak\n", __func__);
}
audio.clear();

6
examples/talk/gpt-2.cpp
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@ -191,9 +191,9 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
// create the ggml context
{
struct ggml_init_params params = {
.mem_size = ctx_size,
.mem_buffer = NULL,
.no_alloc = false,
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
model.ctx = ggml_init(params);

5
examples/talk/talk.cpp
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@ -349,7 +349,10 @@ int main(int argc, char ** argv) {
gpt2_set_prompt(ctx_gpt, prompt_base.c_str());
text_to_speak = ::replace(text_to_speak, params.person + ": ", "");
system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
int ret = system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
if (ret != 0) {
fprintf(stderr, "%s: system() failed!\n", __func__);
}
audio.clear();