mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-06-25 13:50:20 +00:00
Matt Thompson
1794 lines
62 KiB
C++
1794 lines
62 KiB
C++
#include "server-task.h"
|
|
|
|
#include "build-info.h"
|
|
#include "server-chat.h"
|
|
#include "chat.h"
|
|
#include "common.h"
|
|
#include "json-schema-to-grammar.h"
|
|
#include "llama.h"
|
|
#include "sampling.h"
|
|
#include "speculative.h"
|
|
#include "server-common.h"
|
|
|
|
using json = nlohmann::ordered_json;
|
|
|
|
//
|
|
// task_params
|
|
//
|
|
|
|
json task_params::format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) const {
|
|
json data = json::array();
|
|
for (const auto & lb : logit_bias) {
|
|
data.push_back(json{
|
|
{"bias", lb.bias},
|
|
{"token", lb.token},
|
|
});
|
|
}
|
|
return data;
|
|
}
|
|
|
|
json task_params::to_json(bool only_metrics) const {
|
|
std::vector<std::string> samplers;
|
|
samplers.reserve(sampling.samplers.size());
|
|
for (const auto & sampler : sampling.samplers) {
|
|
samplers.emplace_back(common_sampler_type_to_str(sampler));
|
|
}
|
|
|
|
json lora = json::array();
|
|
for (auto & it : this->lora) {
|
|
lora.push_back({{"id", it.first}, {"scale", it.second}});
|
|
}
|
|
|
|
if (only_metrics) {
|
|
return json {
|
|
{"seed", sampling.seed},
|
|
{"temperature", sampling.temp},
|
|
{"dynatemp_range", sampling.dynatemp_range},
|
|
{"dynatemp_exponent", sampling.dynatemp_exponent},
|
|
{"top_k", sampling.top_k},
|
|
{"top_p", sampling.top_p},
|
|
{"min_p", sampling.min_p},
|
|
{"top_n_sigma", sampling.top_n_sigma},
|
|
{"xtc_probability", sampling.xtc_probability},
|
|
{"xtc_threshold", sampling.xtc_threshold},
|
|
{"typical_p", sampling.typ_p},
|
|
{"repeat_last_n", sampling.penalty_last_n},
|
|
{"repeat_penalty", sampling.penalty_repeat},
|
|
{"presence_penalty", sampling.penalty_present},
|
|
{"frequency_penalty", sampling.penalty_freq},
|
|
{"dry_multiplier", sampling.dry_multiplier},
|
|
{"dry_base", sampling.dry_base},
|
|
{"dry_allowed_length", sampling.dry_allowed_length},
|
|
{"dry_penalty_last_n", sampling.dry_penalty_last_n},
|
|
{"mirostat", sampling.mirostat},
|
|
{"mirostat_tau", sampling.mirostat_tau},
|
|
{"mirostat_eta", sampling.mirostat_eta},
|
|
{"max_tokens", n_predict},
|
|
{"n_predict", n_predict}, // TODO: deduplicate?
|
|
{"n_keep", n_keep},
|
|
{"n_discard", n_discard},
|
|
{"ignore_eos", sampling.ignore_eos},
|
|
{"stream", stream},
|
|
{"n_probs", sampling.n_probs},
|
|
{"min_keep", sampling.min_keep},
|
|
{"chat_format", common_chat_format_name(chat_parser_params.format)},
|
|
{"reasoning_format", common_reasoning_format_name(chat_parser_params.reasoning_format)},
|
|
{"reasoning_in_content", chat_parser_params.reasoning_in_content},
|
|
{"generation_prompt", chat_parser_params.generation_prompt},
|
|
{"samplers", samplers},
|
|
{"speculative.types", common_speculative_type_name_str(speculative.types)},
|
|
{"timings_per_token", timings_per_token},
|
|
{"post_sampling_probs", post_sampling_probs},
|
|
{"backend_sampling", sampling.backend_sampling},
|
|
{"lora", lora},
|
|
};
|
|
}
|
|
|
|
auto grammar_triggers = json::array();
|
|
for (const auto & trigger : sampling.grammar_triggers) {
|
|
server_grammar_trigger ct(trigger);
|
|
grammar_triggers.push_back(ct.to_json());
|
|
}
|
|
|
|
return json {
|
|
{"seed", sampling.seed},
|
|
{"temperature", sampling.temp},
|
|
{"dynatemp_range", sampling.dynatemp_range},
|
|
{"dynatemp_exponent", sampling.dynatemp_exponent},
|
|
{"top_k", sampling.top_k},
|
|
{"top_p", sampling.top_p},
|
|
{"min_p", sampling.min_p},
|
|
{"top_n_sigma", sampling.top_n_sigma},
|
|
{"xtc_probability", sampling.xtc_probability},
|
|
{"xtc_threshold", sampling.xtc_threshold},
|
|
{"typical_p", sampling.typ_p},
|
|
{"repeat_last_n", sampling.penalty_last_n},
|
|
{"repeat_penalty", sampling.penalty_repeat},
|
|
{"presence_penalty", sampling.penalty_present},
|
|
{"frequency_penalty", sampling.penalty_freq},
|
|
{"dry_multiplier", sampling.dry_multiplier},
|
|
{"dry_base", sampling.dry_base},
|
|
{"dry_allowed_length", sampling.dry_allowed_length},
|
|
{"dry_penalty_last_n", sampling.dry_penalty_last_n},
|
|
{"dry_sequence_breakers", sampling.dry_sequence_breakers},
|
|
{"mirostat", sampling.mirostat},
|
|
{"mirostat_tau", sampling.mirostat_tau},
|
|
{"mirostat_eta", sampling.mirostat_eta},
|
|
{"stop", antiprompt},
|
|
{"max_tokens", n_predict},
|
|
{"n_predict", n_predict}, // TODO: deduplicate?
|
|
{"n_keep", n_keep},
|
|
{"n_discard", n_discard},
|
|
{"ignore_eos", sampling.ignore_eos},
|
|
{"stream", stream},
|
|
{"logit_bias", format_logit_bias(sampling.logit_bias)},
|
|
{"n_probs", sampling.n_probs},
|
|
{"min_keep", sampling.min_keep},
|
|
{"grammar", common_grammar_value(sampling.grammar)},
|
|
{"grammar_lazy", sampling.grammar_lazy},
|
|
{"grammar_triggers", grammar_triggers},
|
|
{"preserved_tokens", sampling.preserved_tokens},
|
|
{"chat_format", common_chat_format_name(chat_parser_params.format)},
|
|
{"reasoning_format", common_reasoning_format_name(chat_parser_params.reasoning_format)},
|
|
{"reasoning_in_content", chat_parser_params.reasoning_in_content},
|
|
{"generation_prompt", chat_parser_params.generation_prompt},
|
|
{"samplers", samplers},
|
|
{"speculative.types", common_speculative_type_name_str(speculative.types)},
|
|
{"timings_per_token", timings_per_token},
|
|
{"post_sampling_probs", post_sampling_probs},
|
|
{"backend_sampling", sampling.backend_sampling},
|
|
{"lora", lora},
|
|
};
|
|
}
|
|
|
|
//
|
|
// task_result_state
|
|
//
|
|
task_result_state::task_result_state(const common_chat_parser_params & chat_parser_params)
|
|
: chat_parser_params(chat_parser_params)
|
|
, oai_resp_id("resp_" + random_string())
|
|
, oai_resp_reasoning_id("rs_" + random_string())
|
|
, oai_resp_message_id("msg_" + random_string()) {
|
|
if (chat_parser_params.is_continuation && !chat_parser_params.echo) {
|
|
// initialize chat_msg to avoid emitting a delta containing the assistant prefill
|
|
chat_msg = common_chat_parse("", true, chat_parser_params);
|
|
}
|
|
}
|
|
|
|
common_chat_msg task_result_state::update_chat_msg(
|
|
const std::string & text_added,
|
|
bool is_partial,
|
|
std::vector<common_chat_msg_diff> & diffs,
|
|
bool filter_tool_calls) {
|
|
generated_text += text_added;
|
|
auto msg_prv_copy = chat_msg;
|
|
//SRV_DBG("Parsing chat message: %s\n", generated_text.c_str());
|
|
auto new_msg = common_chat_parse(
|
|
generated_text,
|
|
is_partial,
|
|
chat_parser_params);
|
|
if (!new_msg.empty()) {
|
|
new_msg.set_tool_call_ids(generated_tool_call_ids, gen_tool_call_id);
|
|
chat_msg = new_msg;
|
|
auto all_diffs = common_chat_msg_diff::compute_diffs(msg_prv_copy, chat_msg);
|
|
|
|
if (!filter_tool_calls) {
|
|
diffs = std::move(all_diffs);
|
|
} else {
|
|
for (auto & d : all_diffs) {
|
|
// If this is a new type of delta, flush all currently pending tool call names
|
|
for (size_t i = 0; i < chat_msg.tool_calls.size(); ++i) {
|
|
if (sent_tool_call_names.count(i) || chat_msg.tool_calls[i].name.empty()) {
|
|
continue;
|
|
}
|
|
if (d.tool_call_index != i || !d.tool_call_delta.arguments.empty()) {
|
|
common_chat_msg_diff header;
|
|
header.tool_call_index = i;
|
|
header.tool_call_delta.id = chat_msg.tool_calls[i].id;
|
|
header.tool_call_delta.name = chat_msg.tool_calls[i].name;
|
|
diffs.push_back(std::move(header));
|
|
sent_tool_call_names.insert(i);
|
|
}
|
|
}
|
|
|
|
if (d.tool_call_index == std::string::npos) {
|
|
diffs.push_back(std::move(d));
|
|
} else {
|
|
size_t i = d.tool_call_index;
|
|
if (sent_tool_call_names.count(i)) {
|
|
if (!d.tool_call_delta.arguments.empty()) {
|
|
d.tool_call_delta.name = "";
|
|
d.tool_call_delta.id = "";
|
|
diffs.push_back(std::move(d));
|
|
}
|
|
} else {
|
|
// Not sent yet.
|
|
if (!d.tool_call_delta.arguments.empty() || !is_partial) {
|
|
d.tool_call_delta.name = chat_msg.tool_calls[i].name;
|
|
d.tool_call_delta.id = chat_msg.tool_calls[i].id;
|
|
diffs.push_back(std::move(d));
|
|
sent_tool_call_names.insert(i);
|
|
} else {
|
|
// Suppress
|
|
}
|
|
}
|
|
}
|
|
}
|
|
// Final check at EOF
|
|
if (!is_partial) {
|
|
for (size_t i = 0; i < chat_msg.tool_calls.size(); ++i) {
|
|
if (!sent_tool_call_names.count(i) && !chat_msg.tool_calls[i].name.empty()) {
|
|
common_chat_msg_diff header;
|
|
header.tool_call_index = i;
|
|
header.tool_call_delta.id = chat_msg.tool_calls[i].id;
|
|
header.tool_call_delta.name = chat_msg.tool_calls[i].name;
|
|
diffs.push_back(std::move(header));
|
|
sent_tool_call_names.insert(i);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
return chat_msg;
|
|
}
|
|
|
|
//
|
|
|
|
// result_timings
|
|
//
|
|
|
|
json result_timings::to_json() const {
|
|
json base = {
|
|
{"cache_n", cache_n},
|
|
|
|
{"prompt_n", prompt_n},
|
|
{"prompt_ms", prompt_ms},
|
|
{"prompt_per_token_ms", prompt_per_token_ms},
|
|
{"prompt_per_second", prompt_per_second},
|
|
|
|
{"predicted_n", predicted_n},
|
|
{"predicted_ms", predicted_ms},
|
|
{"predicted_per_token_ms", predicted_per_token_ms},
|
|
{"predicted_per_second", predicted_per_second},
|
|
};
|
|
|
|
if (draft_n > 0) {
|
|
base["draft_n"] = draft_n;
|
|
base["draft_n_accepted"] = draft_n_accepted;
|
|
}
|
|
|
|
return base;
|
|
}
|
|
|
|
//
|
|
// result_prompt_progress
|
|
//
|
|
json result_prompt_progress::to_json() const {
|
|
return json {
|
|
{"total", total},
|
|
{"cache", cache},
|
|
{"processed", processed},
|
|
{"time_ms", time_ms},
|
|
};
|
|
}
|
|
|
|
static inline std::string stop_type_to_str(stop_type type) {
|
|
switch (type) {
|
|
case STOP_TYPE_EOS: return "eos";
|
|
case STOP_TYPE_WORD: return "word";
|
|
case STOP_TYPE_LIMIT: return "limit";
|
|
default: return "none";
|
|
}
|
|
}
|
|
|
|
//
|
|
// completion_token_output
|
|
//
|
|
|
|
json completion_token_output::to_json(bool post_sampling_probs) const {
|
|
json probs_for_token = json::array();
|
|
for (const auto & p : probs) {
|
|
std::string txt(p.txt);
|
|
txt.resize(validate_utf8(txt));
|
|
probs_for_token.push_back(json {
|
|
{"id", p.tok},
|
|
{"token", txt},
|
|
{"bytes", str_to_bytes(p.txt)},
|
|
{
|
|
post_sampling_probs ? "prob" : "logprob",
|
|
post_sampling_probs ? p.prob : logarithm(p.prob)
|
|
},
|
|
});
|
|
}
|
|
return probs_for_token;
|
|
}
|
|
|
|
json completion_token_output::probs_vector_to_json(const std::vector<completion_token_output> & probs, bool post_sampling_probs) {
|
|
json out = json::array();
|
|
for (const auto & p : probs) {
|
|
std::string txt(p.text_to_send);
|
|
txt.resize(validate_utf8(txt));
|
|
out.push_back(json {
|
|
{"id", p.tok},
|
|
{"token", txt},
|
|
{"bytes", str_to_bytes(p.text_to_send)},
|
|
{
|
|
post_sampling_probs ? "prob" : "logprob",
|
|
post_sampling_probs ? p.prob : logarithm(p.prob)
|
|
},
|
|
{
|
|
post_sampling_probs ? "top_probs" : "top_logprobs",
|
|
p.to_json(post_sampling_probs)
|
|
},
|
|
});
|
|
}
|
|
return out;
|
|
}
|
|
|
|
float completion_token_output::logarithm(float x) {
|
|
// nlohmann::json converts -inf to null, so we need to prevent that
|
|
return x == 0.0f ? std::numeric_limits<float>::lowest() : std::log(x);
|
|
}
|
|
|
|
std::vector<unsigned char> completion_token_output::str_to_bytes(const std::string & str) {
|
|
std::vector<unsigned char> bytes;
|
|
for (unsigned char c : str) {
|
|
bytes.push_back(c);
|
|
}
|
|
return bytes;
|
|
}
|
|
|
|
//
|
|
// server_task_result_cmpl_final
|
|
//
|
|
json server_task_result_cmpl_final::to_json() {
|
|
GGML_ASSERT(is_updated && "update() must be called before to_json()");
|
|
switch (res_type) {
|
|
case TASK_RESPONSE_TYPE_NONE:
|
|
return to_json_non_oaicompat();
|
|
case TASK_RESPONSE_TYPE_OAI_CMPL:
|
|
return to_json_oaicompat();
|
|
case TASK_RESPONSE_TYPE_OAI_CHAT:
|
|
return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
|
|
case TASK_RESPONSE_TYPE_OAI_RESP:
|
|
return stream ? to_json_oaicompat_resp_stream() : to_json_oaicompat_resp();
|
|
case TASK_RESPONSE_TYPE_OAI_ASR:
|
|
return to_json_oaicompat_asr();
|
|
case TASK_RESPONSE_TYPE_ANTHROPIC:
|
|
return stream ? to_json_anthropic_stream() : to_json_anthropic();
|
|
default:
|
|
GGML_ASSERT(false && "Invalid task_response_type");
|
|
}
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_non_oaicompat() {
|
|
json res = json {
|
|
{"index", index},
|
|
{"content", content},
|
|
{"tokens", tokens},
|
|
{"id_slot", id_slot},
|
|
{"stop", true},
|
|
{"model", oaicompat_model},
|
|
{"tokens_predicted", n_decoded},
|
|
{"tokens_evaluated", n_prompt_tokens},
|
|
{"generation_settings", generation_params.to_json()},
|
|
{"prompt", prompt},
|
|
{"has_new_line", has_new_line},
|
|
{"truncated", truncated},
|
|
{"stop_type", stop_type_to_str(stop)},
|
|
{"stopping_word", stopping_word},
|
|
{"tokens_cached", n_tokens_cached},
|
|
{"timings", timings.to_json()},
|
|
};
|
|
if (!stream && !probs_output.empty()) {
|
|
res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
|
|
}
|
|
return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
|
|
}
|
|
|
|
json server_task_result_cmpl_final::usage_json_oaicompat() {
|
|
return json {
|
|
{"completion_tokens", n_decoded},
|
|
{"prompt_tokens", n_prompt_tokens},
|
|
{"total_tokens", n_decoded + n_prompt_tokens},
|
|
{"prompt_tokens_details", json { {"cached_tokens", n_prompt_tokens_cache} }},
|
|
};
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_oaicompat() {
|
|
std::time_t t = std::time(0);
|
|
json logprobs = json(nullptr); // OAI default to null
|
|
if (!stream && probs_output.size() > 0) {
|
|
logprobs = json{
|
|
{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
|
|
};
|
|
}
|
|
json finish_reason = "length";
|
|
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
finish_reason = "stop";
|
|
}
|
|
json res = json {
|
|
{"choices", json::array({
|
|
json{
|
|
{"text", content},
|
|
{"index", index},
|
|
{"logprobs", logprobs},
|
|
{"finish_reason", finish_reason},
|
|
}
|
|
})},
|
|
{"created", t},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "text_completion"},
|
|
{"usage", usage_json_oaicompat()},
|
|
{"id", oaicompat_cmpl_id}
|
|
};
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose) {
|
|
res["__verbose"] = to_json_non_oaicompat();
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
res.push_back({"timings", timings.to_json()});
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_oaicompat_chat() {
|
|
std::string finish_reason = "length";
|
|
common_chat_msg msg;
|
|
if (!oaicompat_msg.empty()) {
|
|
msg = oaicompat_msg;
|
|
} else {
|
|
msg.role = "assistant";
|
|
msg.content = content;
|
|
}
|
|
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
|
|
}
|
|
|
|
json choice {
|
|
{"finish_reason", finish_reason},
|
|
{"index", index},
|
|
{"message", msg.to_json_oaicompat()},
|
|
};
|
|
|
|
if (!stream && probs_output.size() > 0) {
|
|
choice["logprobs"] = json{
|
|
{"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
|
|
};
|
|
}
|
|
|
|
std::time_t t = std::time(0);
|
|
|
|
json res = json {
|
|
{"choices", json::array({choice})},
|
|
{"created", t},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "chat.completion"},
|
|
{"usage", usage_json_oaicompat()},
|
|
{"id", oaicompat_cmpl_id}
|
|
};
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose) {
|
|
res["__verbose"] = to_json_non_oaicompat();
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
res.push_back({"timings", timings.to_json()});
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
|
|
std::time_t t = std::time(0);
|
|
std::string finish_reason = "length";
|
|
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls";
|
|
}
|
|
|
|
json deltas = json::array();
|
|
for (const auto & diff : oaicompat_msg_diffs) {
|
|
deltas.push_back({
|
|
{"choices", json::array({
|
|
json {
|
|
{"finish_reason", nullptr},
|
|
{"index", index},
|
|
{"delta", server_chat_msg_diff_to_json_oaicompat(diff)},
|
|
},
|
|
})},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "chat.completion.chunk"},
|
|
});
|
|
}
|
|
|
|
deltas.push_back({
|
|
{"choices", json::array({
|
|
json {
|
|
{"finish_reason", finish_reason},
|
|
{"index", index},
|
|
{"delta", json::object()},
|
|
},
|
|
})},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "chat.completion.chunk"},
|
|
});
|
|
|
|
if (include_usage) {
|
|
// OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage
|
|
// https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices
|
|
deltas.push_back({
|
|
{"choices", json::array()},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "chat.completion.chunk"},
|
|
{"usage", usage_json_oaicompat()},
|
|
});
|
|
}
|
|
|
|
if (timings.prompt_n >= 0) {
|
|
deltas.back().push_back({"timings", timings.to_json()});
|
|
}
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose && !deltas.empty()) {
|
|
deltas.front()["__verbose"] = to_json_non_oaicompat();
|
|
}
|
|
|
|
return deltas;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_oaicompat_resp() {
|
|
common_chat_msg msg;
|
|
if (!oaicompat_msg.empty()) {
|
|
msg = oaicompat_msg;
|
|
} else {
|
|
msg.role = "assistant";
|
|
msg.content = content;
|
|
}
|
|
|
|
std::vector<json> output;
|
|
|
|
if (msg.reasoning_content != "") {
|
|
output.push_back(json {
|
|
{"id", "rs_" + random_string()},
|
|
{"summary", json::array()},
|
|
{"type", "reasoning"},
|
|
{"content", json::array({ json {
|
|
{"text", msg.reasoning_content},
|
|
{"type", "reasoning_text"},
|
|
}})},
|
|
{"encrypted_content", ""},
|
|
{"status", "completed"},
|
|
});
|
|
}
|
|
|
|
if (msg.content != "") {
|
|
output.push_back(json {
|
|
{"content", json::array({ json {
|
|
{"type", "output_text"},
|
|
{"annotations", json::array()},
|
|
{"logprobs", json::array()},
|
|
{"text", msg.content},
|
|
}})},
|
|
{"id", "msg_" + random_string()},
|
|
{"role", msg.role},
|
|
{"status", "completed"},
|
|
{"type", "message"},
|
|
});
|
|
}
|
|
|
|
for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
|
|
output.push_back(json {
|
|
{"id", "fc_" + tool_call.id},
|
|
{"type", "function_call"},
|
|
{"status", "completed"},
|
|
{"arguments", tool_call.arguments},
|
|
{"call_id", "call_" + tool_call.id},
|
|
{"name", tool_call.name},
|
|
});
|
|
}
|
|
|
|
std::time_t t = std::time(0);
|
|
json res = {
|
|
{"completed_at", t},
|
|
{"created_at", t},
|
|
{"id", oai_resp_id},
|
|
{"model", oaicompat_model},
|
|
{"object", "response"},
|
|
{"output", output},
|
|
{"status", "completed"},
|
|
{"usage", json {
|
|
{"input_tokens", n_prompt_tokens},
|
|
{"output_tokens", n_decoded},
|
|
{"total_tokens", n_decoded + n_prompt_tokens},
|
|
{"input_tokens_details", json { {"cached_tokens", n_prompt_tokens_cache} }},
|
|
}},
|
|
};
|
|
|
|
return res;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
|
|
std::vector<json> server_sent_events;
|
|
std::vector<json> output;
|
|
|
|
if (oaicompat_msg.reasoning_content != "") {
|
|
const json output_item = json {
|
|
{"id", oai_resp_reasoning_id},
|
|
{"summary", json::array()},
|
|
{"type", "reasoning"},
|
|
{"content", json::array({ json {
|
|
{"text", oaicompat_msg.reasoning_content},
|
|
{"type", "reasoning_text"},
|
|
}})},
|
|
{"encrypted_content", ""},
|
|
};
|
|
|
|
server_sent_events.push_back(json {
|
|
{"event", "response.output_item.done"},
|
|
{"data", json {
|
|
{"type", "response.output_item.done"},
|
|
{"item", output_item}
|
|
}}
|
|
});
|
|
output.push_back(output_item);
|
|
}
|
|
|
|
if (oaicompat_msg.content != "") {
|
|
server_sent_events.push_back(json {
|
|
{"event", "response.output_text.done"},
|
|
{"data", json {
|
|
{"type", "response.output_text.done"},
|
|
{"item_id", oai_resp_message_id},
|
|
{"text", oaicompat_msg.content}
|
|
}}
|
|
});
|
|
|
|
const json content_part = {
|
|
{"type", "output_text"},
|
|
{"annotations", json::array()},
|
|
{"logprobs", json::array()},
|
|
{"text", oaicompat_msg.content}
|
|
};
|
|
|
|
server_sent_events.push_back(json {
|
|
{"event", "response.content_part.done"},
|
|
{"data", json {
|
|
{"type", "response.content_part.done"},
|
|
{"item_id", oai_resp_message_id},
|
|
{"part", content_part}
|
|
}}
|
|
});
|
|
const json output_item = {
|
|
{"type", "message"},
|
|
{"status", "completed"},
|
|
{"id", oai_resp_message_id},
|
|
{"content", json::array({content_part})},
|
|
{"role", "assistant"}
|
|
};
|
|
|
|
server_sent_events.push_back(json {
|
|
{"event", "response.output_item.done"},
|
|
{"data", json {
|
|
{"type", "response.output_item.done"},
|
|
{"item", output_item}
|
|
}}
|
|
});
|
|
output.push_back(output_item);
|
|
}
|
|
|
|
for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
|
|
const json output_item = {
|
|
{"id", "fc_" + tool_call.id},
|
|
{"type", "function_call"},
|
|
{"status", "completed"},
|
|
{"arguments", tool_call.arguments},
|
|
{"call_id", "call_" + tool_call.id},
|
|
{"name", tool_call.name}
|
|
};
|
|
server_sent_events.push_back(json {
|
|
{"event", "response.output_item.done"},
|
|
{"data", json {
|
|
{"type", "response.output_item.done"},
|
|
{"item", output_item}
|
|
}}
|
|
});
|
|
output.push_back(output_item);
|
|
}
|
|
|
|
std::time_t t = std::time(0);
|
|
server_sent_events.push_back(json {
|
|
{"event", "response.completed"},
|
|
{"data", json {
|
|
{"type", "response.completed"},
|
|
{"response", json {
|
|
{"id", oai_resp_id},
|
|
{"object", "response"},
|
|
{"created_at", t},
|
|
{"status", "completed"},
|
|
{"model", oaicompat_model},
|
|
{"output", output},
|
|
{"usage", json {
|
|
{"input_tokens", n_prompt_tokens},
|
|
{"output_tokens", n_decoded},
|
|
{"total_tokens", n_decoded + n_prompt_tokens},
|
|
{"input_tokens_details", json { {"cached_tokens", n_prompt_tokens_cache} }},
|
|
}}
|
|
}},
|
|
}}
|
|
});
|
|
|
|
return server_sent_events;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_oaicompat_asr() {
|
|
json event = json {
|
|
{"type", "transcript.text.done"},
|
|
{"text", oaicompat_msg.content},
|
|
{"usage", json {
|
|
{"type", "tokens"},
|
|
{"input_tokens", n_prompt_tokens},
|
|
{"output_tokens", n_decoded},
|
|
{"total_tokens", n_decoded + n_prompt_tokens},
|
|
{"input_tokens_details", json { {"cached_tokens", n_prompt_tokens_cache} }},
|
|
}},
|
|
};
|
|
return event;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_anthropic() {
|
|
std::string stop_reason = "max_tokens";
|
|
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
|
|
}
|
|
|
|
json content_blocks = json::array();
|
|
|
|
common_chat_msg msg;
|
|
if (!oaicompat_msg.empty()) {
|
|
msg = oaicompat_msg;
|
|
} else {
|
|
msg.role = "assistant";
|
|
msg.content = content;
|
|
}
|
|
|
|
// thinking block comes first (Anthropic extended thinking format)
|
|
if (!msg.reasoning_content.empty()) {
|
|
content_blocks.push_back({
|
|
{"type", "thinking"},
|
|
{"thinking", msg.reasoning_content},
|
|
{"signature", ""} // empty signature for local models (no cryptographic verification)
|
|
});
|
|
}
|
|
|
|
if (!msg.content.empty()) {
|
|
content_blocks.push_back({
|
|
{"type", "text"},
|
|
{"text", msg.content}
|
|
});
|
|
}
|
|
|
|
for (const auto & tool_call : msg.tool_calls) {
|
|
json tool_use_block = {
|
|
{"type", "tool_use"},
|
|
{"id", tool_call.id},
|
|
{"name", tool_call.name}
|
|
};
|
|
|
|
try {
|
|
tool_use_block["input"] = json::parse(tool_call.arguments);
|
|
} catch (const std::exception &) {
|
|
tool_use_block["input"] = json::object();
|
|
}
|
|
|
|
content_blocks.push_back(tool_use_block);
|
|
}
|
|
|
|
json res = {
|
|
{"id", oaicompat_cmpl_id},
|
|
{"type", "message"},
|
|
{"role", "assistant"},
|
|
{"content", content_blocks},
|
|
{"model", oaicompat_model},
|
|
{"stop_reason", stop_reason},
|
|
{"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)},
|
|
{"usage", {
|
|
{"cache_read_input_tokens", n_prompt_tokens_cache},
|
|
{"input_tokens", n_prompt_tokens - n_prompt_tokens_cache},
|
|
{"output_tokens", n_decoded}
|
|
}}
|
|
};
|
|
|
|
return res;
|
|
}
|
|
|
|
json server_task_result_cmpl_final::to_json_anthropic_stream() {
|
|
json events = json::array();
|
|
|
|
std::string stop_reason = "max_tokens";
|
|
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
|
stop_reason = oaicompat_msg.tool_calls.empty() ? "end_turn" : "tool_use";
|
|
}
|
|
|
|
bool has_thinking = !oaicompat_msg.reasoning_content.empty();
|
|
bool has_text = !oaicompat_msg.content.empty();
|
|
size_t num_tool_calls = oaicompat_msg.tool_calls.size();
|
|
|
|
// content block indices: thinking (0) -> text (0 or 1) -> tool_use (n+)
|
|
size_t thinking_block_index = 0;
|
|
size_t text_block_index = has_thinking ? 1 : 0;
|
|
|
|
bool thinking_block_started = false;
|
|
bool text_block_started = false;
|
|
std::unordered_set<size_t> tool_calls_started;
|
|
|
|
for (const auto & diff : oaicompat_msg_diffs) {
|
|
// handle thinking/reasoning content
|
|
if (!diff.reasoning_content_delta.empty()) {
|
|
if (!thinking_block_started) {
|
|
events.push_back({
|
|
{"event", "content_block_start"},
|
|
{"data", {
|
|
{"type", "content_block_start"},
|
|
{"index", thinking_block_index},
|
|
{"content_block", {
|
|
{"type", "thinking"},
|
|
{"thinking", ""}
|
|
}}
|
|
}}
|
|
});
|
|
thinking_block_started = true;
|
|
}
|
|
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", thinking_block_index},
|
|
{"delta", {
|
|
{"type", "thinking_delta"},
|
|
{"thinking", diff.reasoning_content_delta}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
|
|
// handle regular text content
|
|
if (!diff.content_delta.empty()) {
|
|
if (!text_block_started) {
|
|
events.push_back({
|
|
{"event", "content_block_start"},
|
|
{"data", {
|
|
{"type", "content_block_start"},
|
|
{"index", text_block_index},
|
|
{"content_block", {
|
|
{"type", "text"},
|
|
{"text", ""}
|
|
}}
|
|
}}
|
|
});
|
|
text_block_started = true;
|
|
}
|
|
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", text_block_index},
|
|
{"delta", {
|
|
{"type", "text_delta"},
|
|
{"text", diff.content_delta}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
|
|
// handle tool calls
|
|
if (diff.tool_call_index != std::string::npos) {
|
|
size_t content_block_index = (has_thinking ? 1 : 0) + (has_text ? 1 : 0) + diff.tool_call_index;
|
|
|
|
if (tool_calls_started.find(diff.tool_call_index) == tool_calls_started.end()) {
|
|
const auto & full_tool_call = oaicompat_msg.tool_calls[diff.tool_call_index];
|
|
|
|
events.push_back({
|
|
{"event", "content_block_start"},
|
|
{"data", {
|
|
{"type", "content_block_start"},
|
|
{"index", content_block_index},
|
|
{"content_block", {
|
|
{"type", "tool_use"},
|
|
{"id", full_tool_call.id},
|
|
{"name", full_tool_call.name}
|
|
}}
|
|
}}
|
|
});
|
|
tool_calls_started.insert(diff.tool_call_index);
|
|
}
|
|
|
|
if (!diff.tool_call_delta.arguments.empty()) {
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", content_block_index},
|
|
{"delta", {
|
|
{"type", "input_json_delta"},
|
|
{"partial_json", diff.tool_call_delta.arguments}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
// close content blocks in order
|
|
if (has_thinking) {
|
|
// Anthropic API requires a signature_delta before closing thinking blocks
|
|
// We use an empty signature since we can't generate a cryptographic signature for local models
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", thinking_block_index},
|
|
{"delta", {
|
|
{"type", "signature_delta"},
|
|
{"signature", ""}
|
|
}}
|
|
}}
|
|
});
|
|
events.push_back({
|
|
{"event", "content_block_stop"},
|
|
{"data", {
|
|
{"type", "content_block_stop"},
|
|
{"index", thinking_block_index}
|
|
}}
|
|
});
|
|
}
|
|
|
|
if (has_text) {
|
|
events.push_back({
|
|
{"event", "content_block_stop"},
|
|
{"data", {
|
|
{"type", "content_block_stop"},
|
|
{"index", text_block_index}
|
|
}}
|
|
});
|
|
}
|
|
|
|
for (size_t i = 0; i < num_tool_calls; i++) {
|
|
size_t content_block_index = (has_thinking ? 1 : 0) + (has_text ? 1 : 0) + i;
|
|
events.push_back({
|
|
{"event", "content_block_stop"},
|
|
{"data", {
|
|
{"type", "content_block_stop"},
|
|
{"index", content_block_index}
|
|
}}
|
|
});
|
|
}
|
|
|
|
events.push_back({
|
|
{"event", "message_delta"},
|
|
{"data", {
|
|
{"type", "message_delta"},
|
|
{"delta", {
|
|
{"stop_reason", stop_reason},
|
|
{"stop_sequence", stopping_word.empty() ? nullptr : json(stopping_word)}
|
|
}},
|
|
{"usage", {
|
|
{"output_tokens", n_decoded}
|
|
}}
|
|
}}
|
|
});
|
|
|
|
events.push_back({
|
|
{"event", "message_stop"},
|
|
{"data", {
|
|
{"type", "message_stop"}
|
|
}}
|
|
});
|
|
|
|
return events;
|
|
}
|
|
|
|
//
|
|
// server_task_result_cmpl_partial
|
|
//
|
|
void server_task_result_cmpl_partial::update(task_result_state & state) {
|
|
is_updated = true;
|
|
if (is_begin) {
|
|
return; // begin marker only flushes headers, skip parsing
|
|
}
|
|
state.update_chat_msg(content, true, oaicompat_msg_diffs);
|
|
|
|
// Copy current state for use in to_json_*() (reflects state BEFORE this chunk)
|
|
thinking_block_started = state.thinking_block_started;
|
|
text_block_started = state.text_block_started;
|
|
|
|
oai_resp_id = state.oai_resp_id;
|
|
oai_resp_reasoning_id = state.oai_resp_reasoning_id;
|
|
oai_resp_message_id = state.oai_resp_message_id;
|
|
oai_resp_fc_id = state.oai_resp_fc_id;
|
|
|
|
// track if the accumulated message has any reasoning content
|
|
anthropic_has_reasoning = !state.chat_msg.reasoning_content.empty();
|
|
|
|
// Pre-compute state updates based on diffs (for next chunk)
|
|
for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
|
|
if (!diff.reasoning_content_delta.empty() && !state.thinking_block_started) {
|
|
state.thinking_block_started = true;
|
|
}
|
|
if (!diff.content_delta.empty() && !state.text_block_started) {
|
|
state.text_block_started = true;
|
|
}
|
|
if (!diff.tool_call_delta.name.empty()) {
|
|
state.oai_resp_fc_id = diff.tool_call_delta.id;
|
|
}
|
|
}
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json() {
|
|
GGML_ASSERT(is_updated && "update() must be called before to_json()");
|
|
if (is_begin) {
|
|
return nullptr; // simply signal to HTTP handler to send the headers and status code
|
|
}
|
|
switch (res_type) {
|
|
case TASK_RESPONSE_TYPE_NONE:
|
|
return to_json_non_oaicompat();
|
|
case TASK_RESPONSE_TYPE_OAI_CMPL:
|
|
return to_json_oaicompat();
|
|
case TASK_RESPONSE_TYPE_OAI_CHAT:
|
|
return to_json_oaicompat_chat();
|
|
case TASK_RESPONSE_TYPE_OAI_RESP:
|
|
return to_json_oaicompat_resp();
|
|
case TASK_RESPONSE_TYPE_OAI_ASR:
|
|
return to_json_oaicompat_asr();
|
|
case TASK_RESPONSE_TYPE_ANTHROPIC:
|
|
return to_json_anthropic();
|
|
default:
|
|
GGML_ASSERT(false && "Invalid task_response_type");
|
|
}
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json_non_oaicompat() {
|
|
// non-OAI-compat JSON
|
|
json res = json {
|
|
{"index", index},
|
|
{"content", content},
|
|
{"tokens", tokens},
|
|
{"stop", false},
|
|
{"id_slot", id_slot},
|
|
{"tokens_predicted", n_decoded},
|
|
{"tokens_evaluated", n_prompt_tokens},
|
|
};
|
|
// populate the timings object when needed (usually for the last response or with timings_per_token enabled)
|
|
if (timings.prompt_n > 0) {
|
|
res.push_back({"timings", timings.to_json()});
|
|
}
|
|
if (is_progress) {
|
|
res.push_back({"prompt_progress", progress.to_json()});
|
|
}
|
|
if (!prob_output.probs.empty()) {
|
|
res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs);
|
|
}
|
|
return res;
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json_oaicompat() {
|
|
std::time_t t = std::time(0);
|
|
json logprobs = json(nullptr); // OAI default to null
|
|
if (prob_output.probs.size() > 0) {
|
|
logprobs = json{
|
|
{"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
|
|
};
|
|
}
|
|
json res = json {
|
|
{"choices", json::array({
|
|
json{
|
|
{"text", content},
|
|
{"index", index},
|
|
{"logprobs", logprobs},
|
|
{"finish_reason", nullptr},
|
|
}
|
|
})},
|
|
{"created", t},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "text_completion"},
|
|
{"id", oaicompat_cmpl_id}
|
|
};
|
|
|
|
// extra fields for debugging purposes
|
|
if (verbose) {
|
|
res["__verbose"] = to_json_non_oaicompat();
|
|
}
|
|
if (timings.prompt_n >= 0) {
|
|
res.push_back({"timings", timings.to_json()});
|
|
}
|
|
if (is_progress) {
|
|
res.push_back({"prompt_progress", progress.to_json()});
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
|
|
bool first = n_decoded == 1;
|
|
std::time_t t = std::time(0);
|
|
json choices;
|
|
|
|
std::vector<json> deltas;
|
|
auto add_delta = [&](const json & delta) {
|
|
deltas.push_back({
|
|
{"choices", json::array({
|
|
json {
|
|
{"finish_reason", nullptr},
|
|
{"index", index},
|
|
{"delta", delta},
|
|
},
|
|
})},
|
|
{"created", t},
|
|
{"id", oaicompat_cmpl_id},
|
|
{"model", oaicompat_model},
|
|
{"system_fingerprint", std::string(llama_build_info())},
|
|
{"object", "chat.completion.chunk"},
|
|
});
|
|
};
|
|
// We have to send an initial update to conform to openai behavior
|
|
if (first || is_progress) {
|
|
add_delta({
|
|
{"role", "assistant"},
|
|
{"content", nullptr},
|
|
});
|
|
}
|
|
|
|
for (const auto & diff : oaicompat_msg_diffs) {
|
|
add_delta(server_chat_msg_diff_to_json_oaicompat(diff));
|
|
}
|
|
|
|
if (!deltas.empty()) {
|
|
auto & last_json = deltas[deltas.size() - 1];
|
|
GGML_ASSERT(last_json.at("choices").size() >= 1);
|
|
|
|
if (prob_output.probs.size() > 0) {
|
|
last_json.at("choices").at(0)["logprobs"] = json {
|
|
{"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
|
|
};
|
|
}
|
|
|
|
if (timings.prompt_n >= 0) {
|
|
last_json.push_back({"timings", timings.to_json()});
|
|
}
|
|
if (is_progress) {
|
|
last_json.push_back({"prompt_progress", progress.to_json()});
|
|
}
|
|
}
|
|
|
|
return deltas;
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
|
|
std::vector<json> events;
|
|
|
|
if (n_decoded == 1) {
|
|
events.push_back(json {
|
|
{"event", "response.created"},
|
|
{"data", json {
|
|
{"type", "response.created"},
|
|
{"response", json {
|
|
{"id", oai_resp_id},
|
|
{"object", "response"},
|
|
{"status", "in_progress"},
|
|
}},
|
|
}},
|
|
});
|
|
events.push_back(json {
|
|
{"event", "response.in_progress"},
|
|
{"data", json {
|
|
{"type", "response.in_progress"},
|
|
{"response", json {
|
|
{"id", oai_resp_id},
|
|
{"object", "response"},
|
|
{"status", "in_progress"},
|
|
}},
|
|
}},
|
|
});
|
|
}
|
|
|
|
for (const common_chat_msg_diff & diff : oaicompat_msg_diffs) {
|
|
if (!diff.reasoning_content_delta.empty()) {
|
|
if (!thinking_block_started) {
|
|
events.push_back(json {
|
|
{"event", "response.output_item.added"},
|
|
{"data", json {
|
|
{"type", "response.output_item.added"},
|
|
{"item", json {
|
|
{"id", oai_resp_reasoning_id},
|
|
{"summary", json::array()},
|
|
{"type", "reasoning"},
|
|
{"content", json::array()},
|
|
{"encrypted_content", ""},
|
|
{"status", "in_progress"},
|
|
}},
|
|
}},
|
|
});
|
|
thinking_block_started = true;
|
|
}
|
|
events.push_back(json {
|
|
{"event", "response.reasoning_text.delta"},
|
|
{"data", json {
|
|
{"type", "response.reasoning_text.delta"},
|
|
{"delta", diff.reasoning_content_delta},
|
|
{"item_id", oai_resp_reasoning_id},
|
|
}},
|
|
});
|
|
}
|
|
|
|
if (!diff.content_delta.empty()) {
|
|
if (!text_block_started) {
|
|
events.push_back(json {
|
|
{"event", "response.output_item.added"},
|
|
{"data", json {
|
|
{"type", "response.output_item.added"},
|
|
{"item", json {
|
|
{"content", json::array()},
|
|
{"id", oai_resp_message_id},
|
|
{"role", "assistant"},
|
|
{"status", "in_progress"},
|
|
{"type", "message"},
|
|
}},
|
|
}},
|
|
});
|
|
events.push_back(json {
|
|
{"event", "response.content_part.added"},
|
|
{"data", json {
|
|
{"type", "response.content_part.added"},
|
|
{"item_id", oai_resp_message_id},
|
|
{"part", json {
|
|
{"type", "output_text"},
|
|
{"text", ""},
|
|
}},
|
|
}},
|
|
});
|
|
text_block_started = true;
|
|
}
|
|
events.push_back(json {
|
|
{"event", "response.output_text.delta"},
|
|
{"data", json {
|
|
{"type", "response.output_text.delta"},
|
|
{"item_id", oai_resp_message_id},
|
|
{"delta", diff.content_delta},
|
|
}},
|
|
});
|
|
}
|
|
|
|
if (!diff.tool_call_delta.name.empty()) {
|
|
events.push_back(json {
|
|
{"event", "response.output_item.added"},
|
|
{"data", json {
|
|
{"type", "response.output_item.added"},
|
|
{"item", json {
|
|
{"id", "fc_" + diff.tool_call_delta.id},
|
|
{"arguments", ""},
|
|
{"call_id", "call_" + diff.tool_call_delta.id},
|
|
{"name", diff.tool_call_delta.name},
|
|
{"type", "function_call"},
|
|
{"status", "in_progress"},
|
|
}},
|
|
}},
|
|
});
|
|
oai_resp_fc_id = diff.tool_call_delta.id;
|
|
}
|
|
|
|
if (!diff.tool_call_delta.arguments.empty()) {
|
|
events.push_back(json {
|
|
{"event", "response.function_call_arguments.delta"},
|
|
{"data", json {
|
|
{"type", "response.function_call_arguments.delta"},
|
|
{"delta", diff.tool_call_delta.arguments},
|
|
{"item_id", "fc_" + oai_resp_fc_id},
|
|
}},
|
|
});
|
|
}
|
|
}
|
|
return events;
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json_oaicompat_asr() {
|
|
json event = json {
|
|
{"type", "transcript.text.delta"},
|
|
{"delta", content},
|
|
};
|
|
return event;
|
|
}
|
|
|
|
json server_task_result_cmpl_partial::to_json_anthropic() {
|
|
json events = json::array();
|
|
bool first = (n_decoded == 1);
|
|
// use member variables to track block state across streaming calls
|
|
// (anthropic_thinking_block_started, anthropic_text_block_started)
|
|
|
|
if (first) {
|
|
events.push_back({
|
|
{"event", "message_start"},
|
|
{"data", {
|
|
{"type", "message_start"},
|
|
{"message", {
|
|
{"id", oaicompat_cmpl_id},
|
|
{"type", "message"},
|
|
{"role", "assistant"},
|
|
{"content", json::array()},
|
|
{"model", oaicompat_model},
|
|
{"stop_reason", nullptr},
|
|
{"stop_sequence", nullptr},
|
|
{"usage", {
|
|
{"cache_read_input_tokens", n_prompt_tokens_cache},
|
|
{"input_tokens", n_prompt_tokens - n_prompt_tokens_cache},
|
|
{"output_tokens", 0}
|
|
}}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
|
|
// content block indices: thinking (0) -> text (0 or 1) -> tool_use (n+)
|
|
size_t thinking_block_index = 0;
|
|
// use anthropic_has_reasoning (set in update()) to know if ANY reasoning was generated
|
|
size_t text_block_index = anthropic_has_reasoning ? 1 : 0;
|
|
|
|
// use local copies of streaming state (copied from task_result_state in update())
|
|
// these reflect the state BEFORE this chunk was processed
|
|
bool thinking_started = thinking_block_started;
|
|
bool text_started = text_block_started;
|
|
|
|
for (const auto & diff : oaicompat_msg_diffs) {
|
|
// handle thinking/reasoning content
|
|
if (!diff.reasoning_content_delta.empty()) {
|
|
if (!thinking_started) {
|
|
events.push_back({
|
|
{"event", "content_block_start"},
|
|
{"data", {
|
|
{"type", "content_block_start"},
|
|
{"index", thinking_block_index},
|
|
{"content_block", {
|
|
{"type", "thinking"},
|
|
{"thinking", ""}
|
|
}}
|
|
}}
|
|
});
|
|
thinking_started = true;
|
|
}
|
|
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", thinking_block_index},
|
|
{"delta", {
|
|
{"type", "thinking_delta"},
|
|
{"thinking", diff.reasoning_content_delta}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
|
|
// handle regular text content
|
|
if (!diff.content_delta.empty()) {
|
|
if (!text_started) {
|
|
events.push_back({
|
|
{"event", "content_block_start"},
|
|
{"data", {
|
|
{"type", "content_block_start"},
|
|
{"index", text_block_index},
|
|
{"content_block", {
|
|
{"type", "text"},
|
|
{"text", ""}
|
|
}}
|
|
}}
|
|
});
|
|
text_started = true;
|
|
}
|
|
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", text_block_index},
|
|
{"delta", {
|
|
{"type", "text_delta"},
|
|
{"text", diff.content_delta}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
|
|
// handle tool calls
|
|
if (diff.tool_call_index != std::string::npos) {
|
|
// use anthropic_has_reasoning for thinking block count (persists across calls)
|
|
size_t content_block_index = (anthropic_has_reasoning ? 1 : 0) + (text_started ? 1 : 0) + diff.tool_call_index;
|
|
|
|
if (!diff.tool_call_delta.name.empty()) {
|
|
events.push_back({
|
|
{"event", "content_block_start"},
|
|
{"data", {
|
|
{"type", "content_block_start"},
|
|
{"index", content_block_index},
|
|
{"content_block", {
|
|
{"type", "tool_use"},
|
|
{"id", diff.tool_call_delta.id},
|
|
{"name", diff.tool_call_delta.name}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
|
|
if (!diff.tool_call_delta.arguments.empty()) {
|
|
events.push_back({
|
|
{"event", "content_block_delta"},
|
|
{"data", {
|
|
{"type", "content_block_delta"},
|
|
{"index", content_block_index},
|
|
{"delta", {
|
|
{"type", "input_json_delta"},
|
|
{"partial_json", diff.tool_call_delta.arguments}
|
|
}}
|
|
}}
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
return events;
|
|
}
|
|
|
|
//
|
|
// server_task_result_embd
|
|
//
|
|
json server_task_result_embd::to_json() {
|
|
return res_type == TASK_RESPONSE_TYPE_OAI_EMBD
|
|
? to_json_oaicompat()
|
|
: to_json_non_oaicompat();
|
|
}
|
|
|
|
json server_task_result_embd::to_json_non_oaicompat() {
|
|
return json {
|
|
{"index", index},
|
|
{"embedding", embedding},
|
|
};
|
|
}
|
|
|
|
json server_task_result_embd::to_json_oaicompat() {
|
|
return json {
|
|
{"index", index},
|
|
{"embedding", embedding[0]},
|
|
{"tokens_evaluated", n_tokens},
|
|
};
|
|
}
|
|
|
|
//
|
|
// server_task_result_rerank
|
|
//
|
|
json server_task_result_rerank::to_json() {
|
|
return json {
|
|
{"index", index},
|
|
{"score", score},
|
|
{"tokens_evaluated", n_tokens},
|
|
};
|
|
}
|
|
|
|
//
|
|
// server_task_result_error
|
|
//
|
|
json server_task_result_error::to_json() {
|
|
json res = format_error_response(err_msg, err_type);
|
|
if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
|
|
res["n_prompt_tokens"] = n_prompt_tokens;
|
|
res["n_ctx"] = n_ctx;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//
|
|
// server_task_result_metrics
|
|
//
|
|
json server_task_result_metrics::to_json() {
|
|
return json {
|
|
{ "idle", n_idle_slots },
|
|
{ "processing", n_processing_slots },
|
|
{ "deferred", n_tasks_deferred },
|
|
{ "t_start", t_start },
|
|
|
|
{ "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total },
|
|
{ "t_tokens_generation_total", t_tokens_generation_total },
|
|
{ "n_tokens_predicted_total", n_tokens_predicted_total },
|
|
{ "t_prompt_processing_total", t_prompt_processing_total },
|
|
|
|
{ "n_tokens_max", n_tokens_max },
|
|
|
|
{ "n_prompt_tokens_processed", n_prompt_tokens_processed },
|
|
{ "t_prompt_processing", t_prompt_processing },
|
|
{ "n_tokens_predicted", n_tokens_predicted },
|
|
{ "t_tokens_generation", t_tokens_generation },
|
|
|
|
{ "n_decode_total", n_decode_total },
|
|
{ "n_busy_slots_total", n_busy_slots_total },
|
|
|
|
{ "slots", slots_data },
|
|
};
|
|
}
|
|
|
|
//
|
|
// server_task_result_slot_save_load
|
|
//
|
|
json server_task_result_slot_save_load::to_json() {
|
|
if (is_save) {
|
|
return json {
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "n_saved", n_tokens },
|
|
{ "n_written", n_bytes },
|
|
{ "timings", {
|
|
{ "save_ms", t_ms }
|
|
}},
|
|
};
|
|
}
|
|
|
|
return json {
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "n_restored", n_tokens },
|
|
{ "n_read", n_bytes },
|
|
{ "timings", {
|
|
{ "restore_ms", t_ms }
|
|
}},
|
|
};
|
|
}
|
|
|
|
//
|
|
// server_task_result_slot_erase
|
|
//
|
|
json server_task_result_slot_erase::to_json() {
|
|
return json {
|
|
{ "id_slot", id_slot },
|
|
{ "n_erased", n_erased },
|
|
};
|
|
}
|
|
|
|
//
|
|
// server_task_result_get_lora
|
|
//
|
|
|
|
json server_task_result_get_lora::to_json() {
|
|
json result = json::array();
|
|
for (size_t i = 0; i < loras.size(); ++i) {
|
|
auto & lora = loras[i];
|
|
json entry = {
|
|
{"id", i},
|
|
{"path", lora.info.path},
|
|
{"scale", lora.info.scale},
|
|
{"task_name", lora.info.task_name},
|
|
{"prompt_prefix", lora.info.prompt_prefix},
|
|
};
|
|
if (!lora.alora_invocation_tokens.empty()) {
|
|
entry["alora_invocation_string"] = lora.alora_invocation_string;
|
|
entry["alora_invocation_tokens"] = lora.alora_invocation_tokens;
|
|
}
|
|
result.push_back(std::move(entry));
|
|
}
|
|
return result;
|
|
}
|
|
|
|
//
|
|
// server_task_result_apply_lora
|
|
//
|
|
|
|
json server_task_result_apply_lora::to_json() {
|
|
return json {{ "success", true }};
|
|
}
|
|
|
|
//
|
|
// server_prompt_cache
|
|
//
|
|
size_t server_prompt_cache::size() const {
|
|
size_t res = 0;
|
|
|
|
for (const auto & state : states) {
|
|
res += state.size();
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
size_t server_prompt_cache::n_tokens() const {
|
|
size_t res = 0;
|
|
|
|
for (const auto & state : states) {
|
|
res += state.n_tokens();
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size_tgt, size_t state_size_dft) {
|
|
// first check if the current state is contained fully in the cache
|
|
for (auto it = states.begin(); it != states.end(); ++it) {
|
|
const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);
|
|
|
|
if (cur_lcp_len == (int) prompt.tokens.size()) {
|
|
SRV_INF("%s", " - prompt is already in the cache, skipping\n");
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
// next, remove any cached prompts that are fully contained in the current prompt
|
|
for (auto it = states.begin(); it != states.end();) {
|
|
const int len = it->tokens.get_common_prefix(prompt.tokens);
|
|
|
|
if (len == (int) it->tokens.size()) {
|
|
SRV_WRN(" - removing obsolete cached prompt with length %d\n", len);
|
|
|
|
it = states.erase(it);
|
|
} else {
|
|
++it;
|
|
}
|
|
}
|
|
|
|
std::vector<uint8_t> state_data_tgt;
|
|
std::vector<uint8_t> state_data_dft;
|
|
|
|
// check if we can allocate enough memory for the new state
|
|
try {
|
|
state_data_tgt.resize(state_size_tgt);
|
|
state_data_dft.resize(state_size_dft);
|
|
} catch (const std::bad_alloc & e) {
|
|
SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what());
|
|
|
|
limit_size = std::max<size_t>(1, 0.4*size());
|
|
|
|
SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0));
|
|
|
|
update();
|
|
|
|
return nullptr;
|
|
}
|
|
|
|
states.push_back({
|
|
/*.tokens =*/ prompt.tokens.clone(),
|
|
/*.data =*/ {
|
|
/*.main =*/ std::move(state_data_tgt),
|
|
/*.drft =*/ std::move(state_data_dft),
|
|
},
|
|
/*.checkpoints =*/ prompt.checkpoints,
|
|
});
|
|
|
|
return &states.back();
|
|
}
|
|
|
|
bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_tgt, llama_context * ctx_dft, int32_t id_slot) {
|
|
const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);
|
|
|
|
float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
|
|
float sim_best = float(lcp_best) / tokens_new.size();
|
|
|
|
SRV_INF(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
|
|
|
|
auto it_best = states.end();
|
|
|
|
// find the most similar cached prompt, that would also preserve the most context
|
|
for (auto it = states.begin(); it != states.end(); ++it) {
|
|
const int lcp_cur = it->tokens.get_common_prefix(tokens_new);
|
|
|
|
const float f_keep_cur = float(lcp_cur) / it->tokens.size();
|
|
const float sim_cur = float(lcp_cur) / tokens_new.size();
|
|
|
|
// don't trash large prompts
|
|
if (f_keep_cur < 0.25f) {
|
|
continue;
|
|
}
|
|
|
|
if (f_keep_best < f_keep_cur && sim_best < sim_cur) {
|
|
f_keep_best = f_keep_cur;
|
|
sim_best = sim_cur;
|
|
|
|
it_best = it;
|
|
}
|
|
}
|
|
|
|
if (it_best != states.end()) {
|
|
SRV_INF(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
|
|
|
|
{
|
|
auto & data = it_best->data.main;
|
|
|
|
const size_t size = data.size();
|
|
const size_t n = llama_state_seq_set_data_ext(ctx_tgt, data.data(), size, id_slot, 0);
|
|
if (n != size) {
|
|
SRV_ERR("failed to restore state with size %zu\n", size);
|
|
|
|
return false;
|
|
}
|
|
|
|
data.clear();
|
|
data.shrink_to_fit();
|
|
}
|
|
|
|
{
|
|
auto & data = it_best->data.drft;
|
|
|
|
if (!data.empty()) {
|
|
GGML_ASSERT(ctx_dft);
|
|
|
|
const size_t size = data.size();
|
|
const size_t n = llama_state_seq_set_data_ext(ctx_dft, data.data(), size, id_slot, 0);
|
|
if (n != size) {
|
|
SRV_WRN("failed to restore state with size %zu\n", size);
|
|
|
|
return false;
|
|
}
|
|
|
|
data.clear();
|
|
data.shrink_to_fit();
|
|
}
|
|
}
|
|
|
|
prompt = std::move(*it_best);
|
|
|
|
states.erase(it_best);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void server_prompt_cache::update() {
|
|
if (limit_size > 0) {
|
|
// always keep at least one state, regardless of the limits
|
|
while (states.size() > 1 && size() > limit_size) {
|
|
if (states.empty()) {
|
|
break;
|
|
}
|
|
|
|
SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0));
|
|
|
|
states.pop_front();
|
|
}
|
|
}
|
|
|
|
// average size per token
|
|
const float size_per_token = std::max<float>(1.0f, float(size()) / (std::max<size_t>(1, n_tokens())));
|
|
|
|
// dynamically increase the token limit if it can fit in the memory limit
|
|
const size_t limit_tokens_cur = limit_size > 0 ? std::max<size_t>(limit_tokens, limit_size/size_per_token) : limit_tokens;
|
|
|
|
if (limit_tokens > 0) {
|
|
while (states.size() > 1 && n_tokens() > limit_tokens_cur) {
|
|
if (states.empty()) {
|
|
break;
|
|
}
|
|
|
|
SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n",
|
|
limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0));
|
|
|
|
states.pop_front();
|
|
}
|
|
}
|
|
|
|
SRV_INF(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
|
|
states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);
|
|
|
|
for (const auto & state : states) {
|
|
SRV_INF(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
|
|
(const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0));
|
|
}
|
|
}
|