Add llama grammar to ensure proper json output

This commit is contained in:
Aaron Po
2026-04-15 13:39:01 -04:00
parent ddf4bcb981
commit 62dfb5e14a
7 changed files with 115 additions and 231 deletions

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@@ -19,7 +19,6 @@
struct llama_model;
struct llama_context;
struct llama_sampler;
/**
* @brief Data generator implementation backed by llama.cpp.
@@ -78,13 +77,9 @@ class LlamaGenerator final : public DataGenerator {
struct ContextDeleter {
void operator()(llama_context* context) const noexcept;
};
struct SamplerDeleter {
void operator()(llama_sampler* sampler) const noexcept;
};
using ModelHandle = std::unique_ptr<llama_model, ModelDeleter>;
using ContextHandle = std::unique_ptr<llama_context, ContextDeleter>;
using SamplerChainHandle = std::unique_ptr<llama_sampler, SamplerDeleter>;
/**
* @brief Loads model and prepares inference context.
@@ -102,20 +97,24 @@ class LlamaGenerator final : public DataGenerator {
* @param system_prompt System role prompt.
* @param prompt User prompt.
* @param max_tokens Maximum tokens to generate.
* @param grammar Optional GBNF grammar constraining generated output.
* @return Generated text.
*/
std::string Infer(const std::string& system_prompt, const std::string& prompt,
int max_tokens = kDefaultMaxTokens);
int max_tokens = kDefaultMaxTokens,
std::string_view grammar = {});
/**
* @brief Runs inference on an already-formatted prompt.
*
* @param formatted_prompt Prompt preformatted for model chat template.
* @param max_tokens Maximum tokens to generate.
* @param grammar Optional GBNF grammar constraining generated output.
* @return Generated text.
*/
std::string InferFormatted(const std::string& formatted_prompt,
int max_tokens = kDefaultMaxTokens);
int max_tokens = kDefaultMaxTokens,
std::string_view grammar = {});
/**
* @brief Loads the brewery system prompt from disk.
@@ -127,8 +126,6 @@ class LlamaGenerator final : public DataGenerator {
ModelHandle model_;
ContextHandle context_;
/// @brief Persistent sampler chain reused across inference calls.
SamplerChainHandle sampler_;
float sampling_temperature_ = 1.0F;
float sampling_top_p_ = kDefaultSamplingTopP;
uint32_t sampling_top_k_ = kDefaultSamplingTopK;

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@@ -14,7 +14,7 @@
struct llama_model;
struct llama_vocab;
typedef int32_t llama_token;
using llama_token = int32_t;
/**
* @brief Normalizes and truncates regional context.
@@ -60,12 +60,4 @@ std::optional<std::string> ValidateBreweryJson(const std::string& raw,
std::string& name_out,
std::string& description_out);
/**
* @brief Extracts the last balanced JSON object from text.
*
* @param text Input text.
* @return Extracted JSON object or an empty string if none exists.
*/
std::string ExtractLastJsonObject(const std::string& text);
#endif // BIERGARTEN_PIPELINE_INCLUDES_DATA_GENERATION_LLAMA_GENERATOR_HELPERS_H_

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@@ -13,7 +13,7 @@
#include "biergarten_data_generator.h"
#include "json_handling/json_loader.h"
static constexpr size_t kBreweryAmount = 4;
static constexpr size_t kBreweryAmount = 50;
std::vector<Location> BiergartenDataGenerator::QueryCitiesWithCountries() {
spdlog::info("\n=== GEOGRAPHIC DATA OVERVIEW ===");

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@@ -6,56 +6,24 @@
#include "data_generation/llama_generator.h"
#include <array>
#include <format>
#include <optional>
#include <stdexcept>
#include <string>
#include <string_view>
#include <spdlog/spdlog.h>
#include "data_generation/llama_generator_helpers.h"
static std::string ExtractFinalJsonPayload(std::string raw_response) {
auto trim = [](const std::string_view text) -> std::string_view {
const size_t first = text.find_first_not_of(" \t\n\r");
if (first == std::string_view::npos) {
return {};
}
const size_t last = text.find_last_not_of(" \t\n\r");
return text.substr(first, last - first + 1);
};
static constexpr std::array<std::string_view, 6> separator_tokens = {
"<|think|>", "<think|>", "<|turn|>",
"<turn|>", "<channel|>", "<|channel|>"};
size_t separator_pos = std::string::npos;
size_t separator_length = 0;
for (const std::string_view token : separator_tokens) {
const size_t candidate_pos = raw_response.rfind(token);
if (candidate_pos != std::string::npos &&
(separator_pos == std::string::npos || candidate_pos > separator_pos)) {
separator_pos = candidate_pos;
separator_length = token.size();
}
}
if (separator_pos != std::string::npos) {
raw_response.erase(0, separator_pos + separator_length);
}
const std::string_view trimmed = trim(raw_response);
const std::string json_candidate =
ExtractLastJsonObject(std::string(trimmed));
if (!json_candidate.empty()) {
return json_candidate;
}
return std::string(trimmed);
}
static constexpr std::string_view kBreweryJsonGrammar = R"json_brewery(
root ::= ws "{" ws "\"name\"" ws ":" ws string ws "," ws "\"description\"" ws ":" ws string ws "}" ws
ws ::= [ \t\n\r]*
string ::= "\"" char+ "\""
char ::= [^"\\\x7F\x00-\x1F] | [\\] escape
escape ::= ["\\/bfnrt] | "u" hex hex hex hex
hex ::= [0-9a-fA-F]
)json_brewery";
BreweryResult LlamaGenerator::GenerateBrewery(
const Location& location, const std::string& region_context) {
@@ -108,7 +76,7 @@ BreweryResult LlamaGenerator::GenerateBrewery(
for (int attempt = 0; attempt < max_attempts; ++attempt) {
constexpr int max_tokens = 1052;
// Generate brewery data from LLM
raw = this->Infer(system_prompt, prompt, max_tokens);
raw = this->Infer(system_prompt, prompt, max_tokens, kBreweryJsonGrammar);
spdlog::debug("LlamaGenerator: raw output (attempt {}): {}", attempt + 1,
raw);
@@ -116,9 +84,8 @@ BreweryResult LlamaGenerator::GenerateBrewery(
std::string name;
std::string description;
const std::string json_only = ExtractFinalJsonPayload(raw);
const std::optional<std::string> validation_error =
ValidateBreweryJson(json_only, name, description);
ValidateBreweryJson(raw, name, description);
if (!validation_error.has_value()) {
// Success: return parsed brewery data
return BreweryResult{.name = std::move(name),

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@@ -11,7 +11,6 @@
#include <boost/json.hpp>
#include <cctype>
#include <optional>
#include <sstream>
#include <stdexcept>
#include <string>
#include <string_view>
@@ -97,11 +96,16 @@ std::string ToChatPrompt(const llama_model* model,
return combined_prompt;
}
const std::array<llama_chat_message, 2> messages = {
{{"system", system_prompt.c_str()}, {"user", user_prompt.c_str()}}};
const std::array<llama_chat_message, 2> messages = {{
{.role = "system", .content = system_prompt.c_str()},
{.role = "user", .content = user_prompt.c_str()},
}};
constexpr std::size_t min_template_buffer_size = 1024;
std::vector<char> buffer(std::max<std::size_t>(
1024, (system_prompt.size() + user_prompt.size()) * 4));
min_template_buffer_size,
(system_prompt.size() + user_prompt.size()) * 4));
auto apply_template_with_resize = [&](const llama_chat_message* chat_messages,
int32_t message_count) -> int32_t {
@@ -113,11 +117,11 @@ std::string ToChatPrompt(const llama_model* model,
return result;
}
if (result >= static_cast<int32_t>(buffer.size())) {
const auto buffer_size = static_cast<int32_t>(buffer.size());
if (result >= buffer_size) {
buffer.resize(static_cast<std::size_t>(result) + 1);
result = llama_chat_apply_template(tmpl, chat_messages, message_count,
true, buffer.data(),
static_cast<int32_t>(buffer.size()));
true, buffer.data(), buffer_size);
}
return result;
@@ -136,8 +140,9 @@ std::string ToChatPrompt(const llama_model* model,
// FALLBACK: If the template fails (e.g., Model rejecting the "system" role),
// combine the system and user prompts into a single "user" message.
const std::array<llama_chat_message, 1> fallback_msg = {
{{"user", combined_prompt.c_str()}}};
const std::array<llama_chat_message, 1> fallback_msg = {{
{.role = "user", .content = combined_prompt.c_str()},
}};
template_result = apply_template_with_resize(fallback_msg.data(), 1);
@@ -188,102 +193,17 @@ void AppendTokenPiece(const llama_vocab* vocab, llama_token token,
"LlamaGenerator: failed to decode sampled token piece");
}
// Shared parser used by the public extractor and JSON validation.
static bool ExtractLastJsonObject(const std::string& text,
std::string& json_out) {
// Remember where the most recent balanced object started.
size_t start = std::string::npos;
// Track nested braces outside of quoted strings.
int depth = 0;
// Track whether the scan is currently inside a quoted string.
bool in_string = false;
// Track escape sequences so quotes inside strings are handled correctly.
bool escaped = false;
// Record whether at least one complete object was found.
bool found = false;
// Keep the latest complete object candidate.
std::string candidate;
// Scan the input text one character at a time.
for (size_t i = 0; i < text.size(); ++i) {
// Inspect the current character.
const char chr = text[i];
// Inside a string literal, only escapes and quotes affect state.
if (in_string) {
if (escaped) {
// The current character was escaped, so clear the escape flag.
escaped = false;
} else if (chr == '\\') {
// Mark the next character as escaped.
escaped = true;
} else if (chr == '"') {
// Closing quote ends the string literal.
in_string = false;
}
continue;
}
// Opening quotes enter string mode.
if (chr == '"') {
in_string = true;
continue;
}
// Opening braces begin or nest a JSON object.
if (chr == '{') {
if (depth == 0) {
// Record the start of the outermost object.
start = i;
}
// Increase nesting depth for the active object.
++depth;
continue;
}
// Closing braces may complete an object.
if (chr == '}') {
if (depth == 0) {
// Ignore stray closing braces.
continue;
}
// Drop one level of nesting.
--depth;
if (depth == 0 && start != std::string::npos) {
// Capture the latest complete object seen so far.
candidate = text.substr(start, i - start + 1);
found = true;
}
}
}
if (!found) {
return false;
}
// Return the captured object text to the caller.
json_out = std::move(candidate);
return true;
}
std::optional<std::string> ValidateBreweryJson(const std::string& raw,
std::string& name_out,
std::string& description_out) {
auto validate_object = [&](const boost::json::value& jv,
auto validate_object = [&](const boost::json::value& json_value,
std::string& error_out) -> bool {
if (!jv.is_object()) {
if (!json_value.is_object()) {
error_out = "JSON root must be an object";
return false;
}
const auto& obj = jv.get_object();
const auto& obj = json_value.get_object();
if (!obj.contains("name") || !obj.at("name").is_string()) {
error_out = "JSON field 'name' is missing or not a string";
return false;
@@ -313,13 +233,14 @@ std::optional<std::string> ValidateBreweryJson(const std::string& raw,
std::string name_lower = name_out;
std::string description_lower = description_out;
std::transform(
name_lower.begin(), name_lower.end(), name_lower.begin(),
[](unsigned char c) { return static_cast<char>(std::tolower(c)); });
std::ranges::transform(name_lower, name_lower.begin(),
[](unsigned char character) {
return static_cast<char>(std::tolower(character));
});
std::transform(description_lower.begin(), description_lower.end(),
description_lower.begin(), [](unsigned char c) {
return static_cast<char>(std::tolower(c));
std::ranges::transform(description_lower, description_lower.begin(),
[](unsigned char character) {
return static_cast<char>(std::tolower(character));
});
if (name_lower == "string" || description_lower == "string") {
@@ -331,41 +252,16 @@ std::optional<std::string> ValidateBreweryJson(const std::string& raw,
return true;
};
boost::system::error_code ec;
boost::json::value jv = boost::json::parse(raw, ec);
boost::system::error_code error_code;
boost::json::value json_value = boost::json::parse(raw, error_code);
std::string validation_error;
if (ec) {
std::string extracted;
if (!ExtractLastJsonObject(raw, extracted)) {
return "JSON parse error: " + ec.message();
if (error_code) {
return "JSON parse error: " + error_code.message();
}
ec.clear();
jv = boost::json::parse(extracted, ec);
if (ec) {
return "JSON parse error: " + ec.message();
}
if (!validate_object(jv, validation_error)) {
if (!validate_object(json_value, validation_error)) {
return validation_error;
}
return std::nullopt;
}
if (!validate_object(jv, validation_error)) {
return validation_error;
}
return std::nullopt;
}
std::string ExtractLastJsonObject(const std::string& text) {
// Reuse the internal parser and return an empty string if none was found.
std::string extracted;
if (ExtractLastJsonObject(text, extracted)) {
return extracted;
}
return {};
}

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@@ -11,6 +11,7 @@
#include <memory>
#include <stdexcept>
#include <string>
#include <string_view>
#include <vector>
#include "data_generation/llama_generator.h"
@@ -19,15 +20,68 @@
static constexpr size_t kPromptTokenSlack = 8;
namespace {
using SamplerHandle = std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
struct SamplerConfig {
float temperature;
uint32_t top_k;
float top_p;
uint32_t seed;
};
SamplerHandle MakeSamplerChain(const llama_vocab* vocab,
const SamplerConfig& config,
std::string_view grammar) {
const llama_sampler_chain_params sampler_params =
llama_sampler_chain_default_params();
SamplerHandle chain(llama_sampler_chain_init(sampler_params),
llama_sampler_free);
if (!chain) {
throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
}
auto add_sampler = [&](llama_sampler* sampler, const char* error_message) {
if (sampler == nullptr) {
throw std::runtime_error(error_message);
}
llama_sampler_chain_add(chain.get(), sampler);
};
if (!grammar.empty()) {
const std::string grammar_text(grammar);
add_sampler(llama_sampler_init_grammar(vocab, grammar_text.c_str(), "root"),
"LlamaGenerator: failed to initialize grammar sampler");
}
add_sampler(llama_sampler_init_temp(config.temperature),
"LlamaGenerator: failed to initialize temperature sampler");
add_sampler(llama_sampler_init_top_k(static_cast<int32_t>(config.top_k)),
"LlamaGenerator: failed to initialize top-k sampler");
add_sampler(llama_sampler_init_top_p(config.top_p, 1),
"LlamaGenerator: failed to initialize top-p sampler");
add_sampler(llama_sampler_init_dist(config.seed),
"LlamaGenerator: failed to initialize distribution sampler");
return chain;
}
} // namespace
std::string LlamaGenerator::Infer(const std::string& system_prompt,
const std::string& prompt,
const int max_tokens) {
const int max_tokens,
std::string_view grammar) {
return InferFormatted(ToChatPrompt(model_.get(), system_prompt, prompt),
max_tokens);
max_tokens, grammar);
}
std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
const int max_tokens) {
const int max_tokens,
std::string_view grammar) {
/**
* Validate that model and context are loaded
*/
@@ -43,6 +97,14 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
throw std::runtime_error("LlamaGenerator: vocab unavailable");
}
const SamplerConfig sampler_config{
.temperature = sampling_temperature_,
.top_k = sampling_top_k_,
.top_p = sampling_top_p_,
.seed = rng_(),
};
auto sampler = MakeSamplerChain(vocab, sampler_config, grammar);
/**
* Clear KV cache to ensure clean inference state (no residual context)
*/
@@ -140,17 +202,13 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
std::vector<llama_token> generated_tokens;
generated_tokens.reserve(static_cast<size_t>(effective_max_tokens));
if (!sampler_) {
throw std::runtime_error("LlamaGenerator: sampler not initialized");
}
for (int i = 0; i < effective_max_tokens; ++i) {
/**
* Sample next token using configured sampler chain and model logits
* Index -1 means use the last output position from previous batch
*/
const llama_token next =
llama_sampler_sample(sampler_.get(), context_.get(), -1);
llama_sampler_sample(sampler.get(), context_.get(), -1);
/**
* Stop if model predicts end-of-generation token (EOS/EOT)
*/

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@@ -30,13 +30,6 @@ void LlamaGenerator::ContextDeleter::operator()(
}
}
void LlamaGenerator::SamplerDeleter::operator()(
llama_sampler* sampler) const noexcept {
if (sampler != nullptr) {
llama_sampler_free(sampler);
}
}
LlamaGenerator::LlamaGenerator(const ApplicationOptions& options,
const std::string& model_path)
: rng_(std::random_device{}()) {
@@ -81,25 +74,6 @@ LlamaGenerator::LlamaGenerator(const ApplicationOptions& options,
n_ctx_ = options.n_ctx;
this->Load(model_path);
const llama_sampler_chain_params sampler_params =
llama_sampler_chain_default_params();
sampler_ = SamplerChainHandle(llama_sampler_chain_init(sampler_params));
if (!sampler_) {
throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
}
llama_sampler_chain_add(sampler_.get(),
llama_sampler_init_temp(sampling_temperature_));
llama_sampler_chain_add(
sampler_.get(),
llama_sampler_init_top_k(static_cast<int32_t>(sampling_top_k_)));
llama_sampler_chain_add(sampler_.get(),
llama_sampler_init_top_p(sampling_top_p_, 1));
llama_sampler_chain_add(sampler_.get(), llama_sampler_init_dist(rng_()));
}
LlamaGenerator::~LlamaGenerator() = default;