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Merge branch 'master' into compilade/refactor-kv-cache

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This commit is contained in:
Francis Couture-Harpin
2024-08-31 21:06:32 -04:00
parent 9b38f8bf65 a47667cff4
commit bc320ef66d
395 changed files with 57725 additions and 169970 deletions
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80
examples/main/main.cpp
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@@ -37,7 +37,8 @@ static gpt_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false;
static bool is_interacting = false;
static bool need_insert_eot = false;
static bool file_exists(const std::string & path) {
std::ifstream f(path.c_str());
@@ -99,7 +100,8 @@ static void write_logfile(
static void sigint_handler(int signo) {
if (signo == SIGINT) {
if (!is_interacting && g_params->interactive) {
is_interacting = true;
is_interacting = true;
need_insert_eot = true;
} else {
console::cleanup();
printf("\n");
@@ -122,6 +124,7 @@ static std::string chat_add_and_format(struct llama_model * model, std::vector<l
auto formatted = llama_chat_format_single(
model, g_params->chat_template, chat_msgs, new_msg, role == "user");
chat_msgs.push_back({role, content});
LOG("formatted: %s\n", formatted.c_str());
return formatted;
}
@@ -204,7 +207,10 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
LOG("%s: load the model and apply lora adapter, if any\n", __func__);
std::tie(model, ctx) = llama_init_from_gpt_params(params);
llama_init_result llama_init = llama_init_from_gpt_params(params);
model = llama_init.model;
ctx = llama_init.context;
if (sparams.cfg_scale > 1.f) {
struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
ctx_guidance = llama_new_context_with_model(model, lparams);
@@ -215,6 +221,40 @@ int main(int argc, char ** argv) {
return 1;
}
LOG("%s: llama threadpool init = n_threads = %d\n",
__func__,
(int) params.cpuparams.n_threads
);
struct ggml_threadpool_params tpp_batch =
ggml_threadpool_params_from_cpu_params(params.cpuparams_batch);
struct ggml_threadpool_params tpp =
ggml_threadpool_params_from_cpu_params(params.cpuparams);
set_process_priority(params.cpuparams.priority);
struct ggml_threadpool * threadpool_batch = NULL;
if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
threadpool_batch = ggml_threadpool_new(&tpp_batch);
if (!threadpool_batch) {
LOG_TEE("%s: batch threadpool create failed : n_threads %d\n", __func__, tpp_batch.n_threads);
exit(1);
}
// Start the non-batch threadpool in the paused state
tpp.paused = true;
}
struct ggml_threadpool * threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
LOG_TEE("%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
exit(1);
}
llama_attach_threadpool(ctx, threadpool, threadpool_batch);
if (ctx_guidance) {
llama_attach_threadpool(ctx_guidance, threadpool, threadpool_batch);
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
LOG("n_ctx: %d\n", n_ctx);
@@ -224,7 +264,14 @@ int main(int argc, char ** argv) {
__func__, n_ctx_train, n_ctx);
}
LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str());
// print chat template example in conversation mode
if (params.conversation) {
if (params.enable_chat_template) {
LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str());
} else {
LOG_TEE("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
}
}
// print system information
{
@@ -254,16 +301,16 @@ int main(int argc, char ** argv) {
}
}
const bool add_bos = llama_should_add_bos_token(model);
const bool add_bos = llama_add_bos_token(model);
if (!llama_model_has_encoder(model)) {
GGML_ASSERT(llama_add_eos_token(model) != 1);
GGML_ASSERT(!llama_add_eos_token(model));
}
LOG("add_bos: %d\n", add_bos);
std::vector<llama_token> embd_inp;
{
auto prompt = (params.conversation && params.enable_chat_template)
auto prompt = (params.conversation && params.enable_chat_template && !params.prompt.empty())
? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode
: params.prompt;
if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
@@ -280,8 +327,13 @@ int main(int argc, char ** argv) {
// Should not run without any tokens
if (embd_inp.empty()) {
embd_inp.push_back(llama_token_bos(model));
LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
if (add_bos) {
embd_inp.push_back(llama_token_bos(model));
LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
} else {
LOG_TEE("error: input is empty\n");
return -1;
}
}
// Tokenize negative prompt
@@ -910,6 +962,13 @@ int main(int argc, char ** argv) {
LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
// if user stop generation mid-way, we must add EOT to finish model's last response
if (need_insert_eot && format_chat) {
llama_token eot = llama_token_eot(model);
embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot);
need_insert_eot = false;
}
embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
@@ -969,6 +1028,9 @@ int main(int argc, char ** argv) {
llama_sampling_free(ctx_sampling);
llama_backend_free();
ggml_threadpool_free(threadpool);
ggml_threadpool_free(threadpool_batch);
#ifndef LOG_DISABLE_LOGS
LOG_TEE("Log end\n");
#endif // LOG_DISABLE_LOGS