ollama/llm/ext_server/utils.hpp

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// MIT License
// Copyright (c) 2023 Georgi Gerganov
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
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#pragma once
#include <string>
#include <vector>
#include <set>
#include <mutex>
#include <condition_variable>
#include <unordered_map>
IBM granite/granitemoe architecture support (#6760) * fix(ext_server): Port llama.cpp sampling refactors to ext_server This was a fairly large changeset. I closely followed the changes here: https://github.com/ggerganov/llama.cpp/commit/df270ef74596da8f1178f08991f4c51f18c9ee82 Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(server.cpp): Refactor server.cpp logging for llama.cpp overhaul Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Bump llama.cpp to the latest master with `granite` support This does not yet have granite MoE support, but that can come in a follow up PR Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(patches): Update all patches (except solar-pro) to work with bumped llama.cpp Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(solar): Update solar patch for llama.cpp bump Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat(llama.cpp): Bump llama.cpp for granitemoe support Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat(llama.cpp): Bump llama.cpp for granitemoe support Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(solar): Update the solar-pro patch for latest llama.cpp bump Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat(llama.cpp): Bump to the latest master of llama.cpp Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(patches): Update all patches for latest bump Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat(llama): Always run sync.sh from the right directory Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama/patches): Update llama patches Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat(llama)!: Rough sync with llama.cpp submodule There are a number of changes that will need to be propagated to llama.go before any of this works! Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama/patches): Add a patch and update for missing ggml-impl.h include This include is where the ggml_cgraph struct is defined. It is included in many of the .c files to define the forward declartion in ggml.h. It seems that with the subset of code included here, the import was somehow lost (or out-of-order) when building, so adding this include to llama.cpp fixes the missing definition. Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama/sync): Add missing ggml-cpu-impl.h copy-over in sync.sh Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Add missing log.cpp This was added as part of the logging overhaul done in llama.cpp Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Overhaul use of sampling module for llama.cpp changes The changes here reflect the changes made in the big llama.cpp sampling PR https://github.com/ggerganov/llama.cpp/pull/9294 The sampling functionality is now broken into the base interface (llama_sampler) and the generation implementation (gpt_sampler). The changes here reflect that. Since the sampling.h/sampling.cpp code uses c++ STL headers, the sampling_ext.[h|cpp] wrapper is maintained to allow go to access a pure-C interface. Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Fix the impl of SampleTokenGreedy for new sampling I don't think this method is currently used, so it could probably just be removed so that all sampling goes through the GPT interface, but in the interest of doing no harm, this should keep the method working as expected. Branch: IBMGraniteArchitectureSupport * fix(llama): Remove unused SampleTokenGreedy Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(sync): Remove bash-specific change to sync.sh Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * chore(gofumpt): Format on llama.go to pass linting Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llm): Fix missing <thread> include in ext_server Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Remove TODO about grammar_first This feature was not used/needed previously so should be fine without plumbing it through now. Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Better naming for sampling wrapper and args Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Fix patch 05 to use new wrapper api and re-sync Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * runner: Flush pending responses before returning If there are any pending reponses (such as from potential stop tokens) then we should send them back before ending the sequence. Otherwise, we can be missing tokens at the end of a response. Fixes #6707 * fix(llama/sampling): Use gpt_sampler with a forward declaration Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llama): Remove unnecessary patch for gguf impl header This was caused by an earlier mistake in the embeddings patch that was dereferencing the pointer instead of using the wrapper API. Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(llm): Remove use of deprecated --log-disable flag Branch: IBMGraniteArchitectureSupport Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
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#include <random>
#include <iostream>
#include <thread>
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#include "json.hpp"
#include "../llava/clip.h"
using json = nlohmann::json;
extern bool server_verbose;
extern bool server_log_json;
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
#endif
#if SERVER_VERBOSE != 1
#define LOG_VERBOSE(MSG, ...)
#else
#define LOG_VERBOSE(MSG, ...) \
do \
{ \
if (server_verbose) \
{ \
server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \
} \
} while (0)
#endif
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#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_DEBUG( MSG, ...) server_log("DEBUG", __func__, __LINE__, MSG, __VA_ARGS__)
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enum server_state {
SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
SERVER_STATE_READY, // Server is ready and model is loaded
SERVER_STATE_ERROR // An error occurred, load_model failed
};
enum task_type {
TASK_TYPE_COMPLETION,
TASK_TYPE_CANCEL,
TASK_TYPE_NEXT_RESPONSE,
TASK_TYPE_METRICS
};
struct task_server {
int id = -1; // to be filled by llama_server_queue
int target_id;
task_type type;
json data;
bool infill_mode = false;
bool embedding_mode = false;
int multitask_id = -1;
};
struct task_result {
int id;
int multitask_id = -1;
bool stop;
bool error;
json result_json;
};
struct task_multi {
int id;
std::set<int> subtasks_remaining{};
std::vector<task_result> results{};
};
// completion token output with probabilities
struct completion_token_output {
struct token_prob
{
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
llama_token tok;
std::string text_to_send;
};
struct token_translator {
llama_context * ctx;
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); }
};
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
std::stringstream ss_tid;
ss_tid << std::this_thread::get_id();
json log = nlohmann::ordered_json{
{"tid", ss_tid.str()},
{"timestamp", time(nullptr)},
};
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if (strncmp("DEBUG", level, strlen(level)) == 0 && !server_verbose) {
return;
}
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if (server_log_json) {
log.merge_patch(
{
{"level", level},
{"function", function},
{"line", line},
{"msg", message},
});
if (!extra.empty()) {
log.merge_patch(extra);
}
std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush;
} else {
if (!extra.empty()) {
log.merge_patch(extra);
}
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std::stringstream ss;
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ss << level << " [" << function << "] " << message << " |";
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for (const auto& el : log.items())
{
const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
ss << " " << el.key() << "=" << value;
}
const std::string str = ss.str();
printf("%.*s\n", (int)str.size(), str.data());
fflush(stdout);
}
}
//
// server utils
//
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value) {
// Fallback null to default value
return body.contains(key) && !body.at(key).is_null()
? body.value(key, default_value)
: default_value;
}
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
inline bool verify_custom_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
std::vector<char> buf(1);
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size());
return res >= 0;
}
// Format given chat. If tmpl is empty, we take the template from model metadata
inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
size_t alloc_size = 0;
// vector holding all allocated string to be passed to llama_chat_apply_template
std::vector<std::string> str(messages.size() * 2);
std::vector<llama_chat_message> chat(messages.size());
for (size_t i = 0; i < messages.size(); ++i) {
auto &curr_msg = messages[i];
str[i*2 + 0] = json_value(curr_msg, "role", std::string(""));
str[i*2 + 1] = json_value(curr_msg, "content", std::string(""));
alloc_size += str[i*2 + 1].length();
chat[i].role = str[i*2 + 0].c_str();
chat[i].content = str[i*2 + 1].c_str();
}
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
std::vector<char> buf(alloc_size * 2);
// run the first time to get the total output length
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
}
std::string formatted_chat(buf.data(), res);
LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
return formatted_chat;
}
//
// work queue utils
//
struct llama_server_queue {
int id = 0;
std::mutex mutex_tasks;
bool running;
// queues
std::vector<task_server> queue_tasks;
std::vector<task_server> queue_tasks_deferred;
std::vector<task_multi> queue_multitasks;
std::condition_variable condition_tasks;
// callback functions
std::function<void(task_server&)> callback_new_task;
std::function<void(task_multi&)> callback_finish_multitask;
std::function<void(void)> callback_run_slots;
// Add a new task to the end of the queue
int post(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
if (task.id == -1) {
task.id = id++;
LOG_VERBOSE("new task id", {{"new_id", task.id}});
}
queue_tasks.push_back(std::move(task));
condition_tasks.notify_one();
return task.id;
}
// Add a new task, but defer until one slot is available
void defer(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
queue_tasks_deferred.push_back(std::move(task));
}
// Get the next id for creating anew task
int get_new_id() {
std::unique_lock<std::mutex> lock(mutex_tasks);
int new_id = id++;
LOG_VERBOSE("new task id", {{"new_id", new_id}});
return new_id;
}
// Register function to process a new task
void on_new_task(std::function<void(task_server&)> callback) {
callback_new_task = callback;
}
// Register function to process a multitask when it is finished
void on_finish_multitask(std::function<void(task_multi&)> callback) {
callback_finish_multitask = callback;
}
// Register the function to be called when all slots data is ready to be processed
void on_run_slots(std::function<void(void)> callback) {
callback_run_slots = callback;
}
// Call when the state of one slot is changed
void notify_slot_changed() {
// move deferred tasks back to main loop
std::unique_lock<std::mutex> lock(mutex_tasks);
for (auto & task : queue_tasks_deferred) {
queue_tasks.push_back(std::move(task));
}
queue_tasks_deferred.clear();
}
// end the start_loop routine
void terminate() {
{
std::unique_lock<std::mutex> lock(mutex_tasks);
running = false;
}
condition_tasks.notify_all();
}
/**
* Main loop consists of these steps:
* - Wait until a new task arrives
* - Process the task (i.e. maybe copy data into slot)
* - Check if multitask is finished
* - Run all slots
*/
void start_loop() {
running = true;
while (true) {
LOG_VERBOSE("new task may arrive", {});
{
while (true)
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
lock.unlock();
break;
}
task_server task = queue_tasks.front();
queue_tasks.erase(queue_tasks.begin());
lock.unlock();
LOG_VERBOSE("callback_new_task", {{"task_id", task.id}});
callback_new_task(task);
}
LOG_VERBOSE("update_multitasks", {});
// check if we have any finished multitasks
auto queue_iterator = queue_multitasks.begin();
while (queue_iterator != queue_multitasks.end())
{
if (queue_iterator->subtasks_remaining.empty())
{
// all subtasks done == multitask is done
task_multi current_multitask = *queue_iterator;
callback_finish_multitask(current_multitask);
// remove this multitask
queue_iterator = queue_multitasks.erase(queue_iterator);
}
else
{
++queue_iterator;
}
}
// all tasks in the current loop is processed, slots data is now ready
LOG_VERBOSE("callback_run_slots", {});
callback_run_slots();
}
LOG_VERBOSE("wait for new task", {});
// wait for new task
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
if (!running) {
LOG_VERBOSE("ending start_loop", {});
return;
}
condition_tasks.wait(lock, [&]{
return (!queue_tasks.empty() || !running);
});
}
}
}
}
//
// functions to manage multitasks
//
// add a multitask by specifying the id of all subtask (subtask is a task_server)
void add_multitask(int multitask_id, std::vector<int>& sub_ids)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
task_multi multi;
multi.id = multitask_id;
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
queue_multitasks.push_back(multi);
}
// updatethe remaining subtasks, while appending results to multitask
void update_multitask(int multitask_id, int subtask_id, task_result& result)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
for (auto& multitask : queue_multitasks)
{
if (multitask.id == multitask_id)
{
multitask.subtasks_remaining.erase(subtask_id);
multitask.results.push_back(result);
}
}
}
};
struct llama_server_response {
typedef std::function<void(int, int, task_result&)> callback_multitask_t;
callback_multitask_t callback_update_multitask;
// for keeping track of all tasks waiting for the result
std::set<int> waiting_task_ids;
// the main result queue
std::vector<task_result> queue_results;
std::mutex mutex_results;
std::condition_variable condition_results;
// add the task_id to the list of tasks waiting for response
void add_waiting_task_id(int task_id) {
LOG_VERBOSE("waiting for task id", {{"task_id", task_id}});
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.insert(task_id);
}
// when the request is finished, we can remove task associated with it
void remove_waiting_task_id(int task_id) {
LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}});
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.erase(task_id);
}
// This function blocks the thread until there is a response for this task_id
task_result recv(int task_id) {
while (true)
{
std::unique_lock<std::mutex> lock(mutex_results);
condition_results.wait(lock, [&]{
return !queue_results.empty();
});
for (int i = 0; i < (int) queue_results.size(); i++)
{
if (queue_results[i].id == task_id)
{
assert(queue_results[i].multitask_id == -1);
task_result res = queue_results[i];
queue_results.erase(queue_results.begin() + i);
return res;
}
}
}
// should never reach here
}
// Register the function to update multitask
void on_multitask_update(callback_multitask_t callback) {
callback_update_multitask = callback;
}
// Send a new result to a waiting task_id
void send(task_result result) {
std::unique_lock<std::mutex> lock(mutex_results);
LOG_VERBOSE("send new result", {{"task_id", result.id}});
for (auto& task_id : waiting_task_ids) {
// LOG_TEE("waiting task id %i \n", task_id);
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
if (result.multitask_id == task_id)
{
LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}});
callback_update_multitask(task_id, result.id, result);
continue;
}
if (result.id == task_id)
{
LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}});
queue_results.push_back(result);
condition_results.notify_all();
return;
}
}
}
};
//
// base64 utils (TODO: move to common in the future)
//
static const std::string base64_chars =
"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz"
"0123456789+/";
static inline bool is_base64(uint8_t c)
{
return (isalnum(c) || (c == '+') || (c == '/'));
}
static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string)
{
int i = 0;
int j = 0;
int in_ = 0;
int in_len = encoded_string.size();
uint8_t char_array_4[4];
uint8_t char_array_3[3];
std::vector<uint8_t> ret;
while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
{
char_array_4[i++] = encoded_string[in_]; in_++;
if (i == 4)
{
for (i = 0; i <4; i++)
{
char_array_4[i] = base64_chars.find(char_array_4[i]);
}
char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (i = 0; (i < 3); i++)
{
ret.push_back(char_array_3[i]);
}
i = 0;
}
}
if (i)
{
for (j = i; j <4; j++)
{
char_array_4[j] = 0;
}
for (j = 0; j <4; j++)
{
char_array_4[j] = base64_chars.find(char_array_4[j]);
}
char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (j = 0; (j < i - 1); j++)
{
ret.push_back(char_array_3[j]);
}
}
return ret;
}
//
// random string / id
//
static std::string random_string()
{
static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
std::random_device rd;
std::mt19937 generator(rd());
std::string result(32, ' ');
for (int i = 0; i < 32; ++i) {
result[i] = str[generator() % str.size()];
}
return result;
}
static std::string gen_chatcmplid()
{
std::stringstream chatcmplid;
chatcmplid << "chatcmpl-" << random_string();
return chatcmplid.str();
}
//
// other common utils
//
static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
{
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
{
}
return i;
}
static bool ends_with(const std::string &str, const std::string &suffix)
{
return str.size() >= suffix.size() &&
0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}
static size_t find_partial_stop_string(const std::string &stop,
const std::string &text)
{
if (!text.empty() && !stop.empty())
{
const char text_last_char = text.back();
for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
{
if (stop[char_index] == text_last_char)
{
const std::string current_partial = stop.substr(0, char_index + 1);
if (ends_with(text, current_partial))
{
return text.size() - char_index - 1;
}
}
}
}
return std::string::npos;
}
// TODO: reuse llama_detokenize
template <class Iter>
static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
{
std::string ret;
for (; begin != end; ++begin)
{
ret += llama_token_to_piece(ctx, *begin);
}
return ret;
}
// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
{
std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
// if the size is 1 and first bit is 1, meaning it's a partial character
// (size > 1 meaning it's already a known token)
if (out.size() == 1 && (out[0] & 0x80) == 0x80)
{
std::stringstream ss;
ss << std::hex << (out[0] & 0xff);
std::string res(ss.str());
out = "byte: \\x" + res;
}
return out;
}
// convert a vector of completion_token_output to json
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
{
json out = json::array();
for (const auto &prob : probs)
{
json probs_for_token = json::array();
for (const auto &p : prob.probs)
{
std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
probs_for_token.push_back(json
{
{"tok_str", tok_str},
{"prob", p.prob},
});
}
std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
out.push_back(json{
{"content", tok_str},
{"probs", probs_for_token},
});
}
return out;
}