Files
ik_llama.cpp/examples/server/utils.hpp
firecoperana d1ae1504c6 Fix non rpc build error (#506)
* Add RPC backend in device list to override tensors.

* rpc : prevent crashes on invalid input (#9040)

Add more checks which prevent RPC server from crashing if invalid input
is received from client
# Conflicts:
#	ggml/src/ggml-rpc.cpp

* rpc : print error message when failed to connect endpoint (#9042)

* Fix RPC error

* Add vulkan, sycl to rpc backend

* add thread in rpc cpu backend

* add cache folder and other improvement in rpc

* add header file

* support for models with non-512 aligned tensors

* rpc : do not wait for response when sending RPC_CMD_SET_TENSOR (#12943)

RPC_CMD_SET_TENSOR always returns an empty response and we send this 4
times per token. We can improve TG speed if we don't wait for this empty
response.

The performance impact of this change depends on the network latency.
# Conflicts:
#	ggml/src/ggml-rpc.cpp

* fix(rpc): Improve input validation and error handling (#13069)

* fix(rpc): Improve input validation and error handling

The `rpc-server` was vulnerable to Denial of Service attacks via
several RPC commands (`SET_TENSOR`, `GRAPH_COMPUTE`, etc.). Malformed
messages could trigger failed assertions (e.g., invalid `ggml_type`)
or out-of-bounds reads/writes leading to `GGML_ABORT` calls,
crashing the server process.

This PR introduces robust input validation and replaces `abort()`
calls with graceful error handling:

- **Type Validation:** `deserialize_tensor` now checks if the
  `tensor->type` is within the valid `GGML_TYPE_COUNT` range
  *before* calling `ggml_new_tensor_4d`. Returns `nullptr` on
  invalid type.
- **Bounds Checks:** Replaced `GGML_ABORT` in `set_tensor`,
  `set_tensor_hash`, and `get_tensor` handlers with error
  logging and returning `false` when data/offset parameters
  are out of buffer bounds.
- **Size Checks:** Added safe arithmetic checks (for overflow) in
  `graph_compute` when calculating required message sizes based
  on client-provided `n_nodes` and `n_tensors`. Returns early
  if the reported sizes conflict with the actual message size or
  would lead to overflow.
- **Error Propagation:**
    - `create_node` now checks for `nullptr` return values from
      `deserialize_tensor` and its recursive calls, propagating
      `nullptr` upwards on failure. Uses `find` instead of `at`
      for safer map access.
    - `copy_tensor` now checks for `nullptr` from `deserialize_tensor`
      and sets the response status to failure if deserialization
      or bounds checks fail.
    - `graph_compute` now checks for `nullptr` return from
      `create_node` and returns failure status correctly. The final
      return value now reflects the actual computation status.

These changes improve the RPC server's resilience
against malformed client requests, preventing crashes and ensuring
errors are handled more gracefully.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* refactor(rpc): address pr comments

removed comments and unnecessary returns

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* refactor(rpc): ambiguous nullptr from create_node

rpc_server::create_node could previously return nullptr if the input ID
was 0 (valid) or if an internal error (deserialization, recursion
failure) occurred (invalid). This ambiguity made error handling
difficult for the caller (`graph_compute`).

This commit clarifies the meaning of nullptr:
- `graph_compute` now checks if the input 'id' was non-zero when
  `create_node` returns nullptr, correctly identifying failures
  versus intentional null links.
- `create_node` avoids recursive calls for zero IDs and propagates
  nullptr unambiguously on failure during recursion.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* refactor(rpc): initial zero check in create_node

The caller (`graph_compute`) already checks `id != 0` when handling
a `nullptr` return from `create_node`, correctly distinguishing
intentional null links from actual errors. This makes the initial
`if (id == 0)` check redundant.

Also removes the log message when a tensor ID is not found in the
provided map which was added in this branch.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* fix(rpc): Handle get_alloc_size failure in server

Check the return value of `server.get_alloc_size` in the RPC server
loop. If the call fails, return early to close the connection.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* refactor(rpc): input size validation in graph_compute

Removes detailed, step-by-step size calculations and overflow
checks in favor of simpler direct comparisons, assuming 64-bit
overflow is unlikely.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* refactor(rpc): remove extra status code setting

Removes the explicit setting of `response.result = GGML_STATUS_FAILED`
when `create_node` returns `nullptr` within `graph_compute`.
Primary signal is the `false` return value in case of failure.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

* refactor(rpc): remove redundant check for tensor->type

Breaks CI on ubuntu-cpu-make. Tensor type is uint32_t, thus
the check is not needed.

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>

---------

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
# Conflicts:
#	ggml/src/ggml-rpc.cpp

* rpc : fix cache directory initialization (#13188)

Signed-off-by: xiaofei <hbuxiaofei@gmail.com>
# Conflicts:
#	examples/rpc/rpc-server.cpp

* rpc : avoid uninitialized memory in serialize_tensor (#13210)

Zero out the name and padding buffers.

* fix merge error

* Add hello command in RPC

* bug fix

* add rpc header

* fix bug for missing rpc names

* add tpc no delay for rpc

* add back webui

* fix rpc function not found error

---------

Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
Signed-off-by: xiaofei <hbuxiaofei@gmail.com>
Co-authored-by: firecoperana <firecoperana>
Co-authored-by: Radoslav Gerganov <rgerganov@gmail.com>
Co-authored-by: matt23456 <matt23456>
Co-authored-by: Ville Vesilehto <ville@vesilehto.fi>
Co-authored-by: xiaofei <hbuxiaofei@gmail.com>
Co-authored-by: Justin Santa Barbara <justinsb@google.com>
2025-06-08 17:27:00 +03:00

466 lines
14 KiB
C++

#pragma once
#include "llama.h"
#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
#include <string>
#include <vector>
#include <sstream>
#include <random>
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::ordered_json;
// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
enum error_type {
ERROR_TYPE_INVALID_REQUEST,
ERROR_TYPE_AUTHENTICATION,
ERROR_TYPE_SERVER,
ERROR_TYPE_NOT_FOUND,
ERROR_TYPE_PERMISSION,
ERROR_TYPE_UNAVAILABLE, // custom error
ERROR_TYPE_NOT_SUPPORTED, // custom error
};
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
#define LOG_ERROR( MSG, ...) server_log("ERR", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra);
template <typename T>
static T json_value(const json & body, const std::string & key, const T & default_value) {
// Fallback null to default value
if (body.contains(key) && !body.at(key).is_null()) {
try {
return body.at(key);
} catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const &) {
std::stringstream ss;
ss << "Wrong type supplied for parameter '" << key << "'. Expected '" << json(default_value).type_name() << "', using default value.";
LOG_WARNING(ss.str().c_str(), body);
return default_value;
}
} else {
return default_value;
}
}
static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra) {
std::stringstream ss_tid;
ss_tid << std::this_thread::get_id();
json log = json{
{"tid", ss_tid.str()},
{"timestamp", time(nullptr)},
};
if (server_log_json) {
log.merge_patch({
{"level", level},
{"function", function},
{"line", line},
{"msg", message},
});
if (!extra.empty()) {
log.merge_patch(extra);
}
printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
} else {
char buf[1024];
snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
if (!extra.empty()) {
log.merge_patch(extra);
}
std::stringstream ss;
ss << buf << " |";
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);
}
//
// chat template utils
//
// 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) {
std::vector<llama_chat_msg> chat;
for (size_t i = 0; i < messages.size(); ++i) {
const auto & curr_msg = messages[i];
std::string role = json_value(curr_msg, "role", std::string(""));
std::string content;
if (curr_msg.contains("content")) {
if (curr_msg["content"].is_string()) {
content = curr_msg["content"].get<std::string>();
} else if (curr_msg["content"].is_array()) {
for (const auto & part : curr_msg["content"]) {
if (part.contains("text")) {
content += "\n" + part["text"].get<std::string>();
}
}
} else {
throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
}
} else {
throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
}
chat.push_back({role, content});
}
auto formatted_chat = llama_chat_apply_template(model, tmpl, chat, true);
LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
return formatted_chat;
}
//
// 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 size_t common_part(const std::string & a, const std::string & 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;
}
struct completion_token_output {
llama_token tok;
std::string text_to_send;
struct token_prob {
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
};
// 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) {
const 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},
});
}
const 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;
}
//
// OAI utils
//
static json oaicompat_completion_params_parse(
const struct llama_model * model,
const json & body, /* openai api json semantics */
const std::string & chat_template) {
json llama_params;
llama_params["__oaicompat"] = true;
// Apply chat template to the list of messages
llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
// Handle "stop" field
if (body.contains("stop") && body.at("stop").is_string()) {
llama_params["stop"] = json::array({body.at("stop").get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Handle "response_format" field
if (body.contains("response_format")) {
json response_format = json_value(body, "response_format", json::object());
std::string response_type = json_value(response_format, "type", std::string());
if (response_type == "json_object") {
llama_params["json_schema"] = json_value(response_format, "schema", json::object());
} else if (!response_type.empty() && response_type != "text") {
throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type);
}
}
// Handle "n" field
int n_choices = json_value(body, "n", 1);
if (n_choices != 1) {
throw std::runtime_error("Only one completion choice is allowed");
}
// Handle "logprobs" field
// TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
if (body.contains("logprobs")) {
llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
} else if (body.contains("top_logprobs")) {
throw std::runtime_error("top_logprobs requires logprobs to be set to true");
}
// Params supported by OAI but unsupported by llama.cpp
static const std::vector<std::string> unsupported_params { "tools", "tool_choice" };
for (auto & param : unsupported_params) {
if (body.contains(param)) {
throw std::runtime_error("Unsupported param: " + param);
}
}
// Copy remaining properties to llama_params
// This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
// See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
for (const auto & item : body.items()) {
// Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
llama_params[item.key()] = item.value();
}
}
return llama_params;
}
static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
return json {
{"tokens", tokens}
};
}
static json format_detokenized_response(const std::string & content) {
return json {
{"content", content}
};
}
static json format_error_response(const std::string & message, const enum error_type type) {
std::string type_str;
int code = 500;
switch (type) {
case ERROR_TYPE_INVALID_REQUEST:
type_str = "invalid_request_error";
code = 400;
break;
case ERROR_TYPE_AUTHENTICATION:
type_str = "authentication_error";
code = 401;
break;
case ERROR_TYPE_NOT_FOUND:
type_str = "not_found_error";
code = 404;
break;
case ERROR_TYPE_SERVER:
type_str = "server_error";
code = 500;
break;
case ERROR_TYPE_PERMISSION:
type_str = "permission_error";
code = 403;
break;
case ERROR_TYPE_NOT_SUPPORTED:
type_str = "not_supported_error";
code = 501;
break;
case ERROR_TYPE_UNAVAILABLE:
type_str = "unavailable_error";
code = 503;
break;
}
return json {
{"code", code},
{"message", message},
{"type", type_str},
};
}