vbatts/llama.cpp
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Merge branch 'master' into xsn/webui_pyodide

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Xuan Son Nguyen 2025-02-08 20:15:07 +01:00
commit 7791845e2c
6 changed files with 24 additions and 19 deletions
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32
examples/server/server.cpp
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@ -334,24 +334,24 @@ struct server_task {
if (data.contains("json_schema") && !data.contains("grammar")) {
try {
auto schema = json_value(data, "json_schema", json::object());
LOG_DBG("JSON schema: %s\n", schema.dump(2).c_str());
SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
params.sampling.grammar = json_schema_to_grammar(schema);
LOG_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
} catch (const std::exception & e) {
throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
}
} else {
params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
LOG_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
LOG_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
}
{
auto it = data.find("chat_format");
if (it != data.end()) {
params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
LOG_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
} else {
params.oaicompat_chat_format = defaults.oaicompat_chat_format;
}
@ -367,12 +367,12 @@ struct server_task {
auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true);
if (ids.size() == 1) {
LOG_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
SRV_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
params.sampling.grammar_trigger_tokens.push_back(ids[0]);
params.sampling.preserved_tokens.insert(ids[0]);
continue;
}
LOG_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
SRV_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
params.sampling.grammar_trigger_words.push_back(trigger);
}
}
@ -381,11 +381,11 @@ struct server_task {
for (const auto & t : *preserved_tokens) {
auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
if (ids.size() == 1) {
LOG_DBG("Preserved token: %d\n", ids[0]);
SRV_DBG("Preserved token: %d\n", ids[0]);
params.sampling.preserved_tokens.insert(ids[0]);
} else {
// This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
LOG_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<std::string>().c_str());
SRV_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<std::string>().c_str());
}
}
}
@ -717,7 +717,7 @@ struct server_task_result_cmpl_final : server_task_result {
std::string finish_reason = "length";
common_chat_msg msg;
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
LOG_DBG("Parsing chat message: %s\n", content.c_str());
SRV_DBG("Parsing chat message: %s\n", content.c_str());
msg = common_chat_parse(content, oaicompat_chat_format);
finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
} else {
@ -1885,7 +1885,7 @@ struct server_context {
}
if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
chat_templates = common_chat_templates_from_model(model, "chatml");
} else {
chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
@ -3355,10 +3355,10 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
// reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch
LOG_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
SRV_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
LOG_DBG("request: %s\n", req.body.c_str());
LOG_DBG("response: %s\n", res.body.c_str());
SRV_DBG("request: %s\n", req.body.c_str());
SRV_DBG("response: %s\n", res.body.c_str());
}
std::function<void(int)> shutdown_handler;
@ -3860,7 +3860,9 @@ int main(int argc, char ** argv) {
try {
const auto & prompt = data.at("prompt");
LOG_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
// TODO: this log can become very long, put it behind a flag or think about a more compact format
//SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
tasks.reserve(tokenized_prompts.size());
for (size_t i = 0; i < tokenized_prompts.size(); i++) {

2
examples/server/webui/src/components/ChatMessage.tsx
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@ -92,7 +92,7 @@ export default function ChatMessage({
<>
<textarea
dir="auto"
className="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
className="textarea textarea-bordered bg-base-100 text-base-content max-w-2xl w-[calc(90vw-8em)] h-24"
value={editingContent}
onChange={(e) => setEditingContent(e.target.value)}
></textarea>

4
ggml/src/ggml-cpu/ggml-cpu.c
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@ -13856,8 +13856,12 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
tp->ec = GGML_STATUS_ABORTED;
}
if (node_n + 1 < cgraph->n_nodes) {
ggml_barrier(state->threadpool);
}
}
ggml_barrier(state->threadpool);
return 0;
}

2
src/llama-model.cpp
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@ -1275,7 +1275,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const bool use_mmap_buffer = true;
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, use_mmap_buffer ? "true" : "false");
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, ml.use_mmap ? "true" : "false");
// build a list of buffer types for the CPU and GPU devices
pimpl->cpu_buft_list = make_cpu_buft_list(devices);

1
src/llama.cpp
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@ -9430,7 +9430,6 @@ static struct llama_model * llama_model_load_from_file_impl(
struct llama_model_params params) {
ggml_time_init();
unsigned cur_percentage = 0;
if (params.progress_callback == NULL) {
params.progress_callback_user_data = &cur_percentage;