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addon.cpp
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#include <stddef.h>
#include <algorithm>
#include <sstream>
#include <vector>
#include "common.h"
#include "llama.h"
#include "common/grammar-parser.h"
#include "napi.h"
class LLAMAModel : public Napi::ObjectWrap<LLAMAModel> {
public:
llama_model_params model_params;
llama_model* model;
LLAMAModel(const Napi::CallbackInfo& info) : Napi::ObjectWrap<LLAMAModel>(info) {
model_params = llama_model_default_params();
// Get the model path
std::string modelPath = info[0].As<Napi::String>().Utf8Value();
if (info.Length() > 1 && info[1].IsObject()) {
Napi::Object options = info[1].As<Napi::Object>();
if (options.Has("gpuLayers")) {
model_params.n_gpu_layers = options.Get("gpuLayers").As<Napi::Number>().Int32Value();
}
if (options.Has("vocabOnly")) {
model_params.vocab_only = options.Get("vocabOnly").As<Napi::Boolean>().Value();
}
if (options.Has("useMmap")) {
model_params.use_mmap = options.Get("useMmap").As<Napi::Boolean>().Value();
}
if (options.Has("useMlock")) {
model_params.use_mlock = options.Get("useMlock").As<Napi::Boolean>().Value();
}
}
llama_backend_init();
model = llama_load_model_from_file(modelPath.c_str(), model_params);
if (model == NULL) {
Napi::Error::New(info.Env(), "Failed to load model").ThrowAsJavaScriptException();
return;
}
}
~LLAMAModel() {
llama_free_model(model);
}
static void init(Napi::Object exports) {
exports.Set("LLAMAModel", DefineClass(exports.Env(), "LLAMAModel", {}));
}
};
class LLAMAGrammar : public Napi::ObjectWrap<LLAMAGrammar> {
public:
grammar_parser::parse_state parsed_grammar;
LLAMAGrammar(const Napi::CallbackInfo& info) : Napi::ObjectWrap<LLAMAGrammar>(info) {
// Get the model path
std::string grammarCode = info[0].As<Napi::String>().Utf8Value();
bool should_print_grammar = false;
if (info.Length() > 1 && info[1].IsObject()) {
Napi::Object options = info[1].As<Napi::Object>();
if (options.Has("printGrammar")) {
should_print_grammar = options.Get("printGrammar").As<Napi::Boolean>().Value();
}
}
parsed_grammar = grammar_parser::parse(grammarCode.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
Napi::Error::New(info.Env(), "Failed to parse grammar").ThrowAsJavaScriptException();
return;
}
if (should_print_grammar) {
grammar_parser::print_grammar(stderr, parsed_grammar);
}
}
static void init(Napi::Object exports) {
exports.Set("LLAMAGrammar", DefineClass(exports.Env(), "LLAMAGrammar", {}));
}
};
class LLAMAGrammarEvaluationState : public Napi::ObjectWrap<LLAMAGrammarEvaluationState> {
public:
LLAMAGrammar* grammarDef;
llama_grammar *grammar = nullptr;
LLAMAGrammarEvaluationState(const Napi::CallbackInfo& info) : Napi::ObjectWrap<LLAMAGrammarEvaluationState>(info) {
grammarDef = Napi::ObjectWrap<LLAMAGrammar>::Unwrap(info[0].As<Napi::Object>());
grammarDef->Ref();
std::vector<const llama_grammar_element *> grammar_rules(grammarDef->parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), grammarDef->parsed_grammar.symbol_ids.at("root")
);
}
~LLAMAGrammarEvaluationState() {
grammarDef->Unref();
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
}
}
static void init(Napi::Object exports) {
exports.Set("LLAMAGrammarEvaluationState", DefineClass(exports.Env(), "LLAMAGrammarEvaluationState", {}));
}
};
class LLAMAContext : public Napi::ObjectWrap<LLAMAContext> {
public:
LLAMAModel* model;
llama_context_params context_params;
llama_context* ctx;
int n_cur = 0;
LLAMAContext(const Napi::CallbackInfo& info) : Napi::ObjectWrap<LLAMAContext>(info) {
model = Napi::ObjectWrap<LLAMAModel>::Unwrap(info[0].As<Napi::Object>());
model->Ref();
context_params = llama_context_default_params();
context_params.seed = -1;
context_params.n_ctx = 4096;
context_params.n_threads = 6;
context_params.n_threads_batch == -1 ? context_params.n_threads : context_params.n_threads_batch;
if (info.Length() > 1 && info[1].IsObject()) {
Napi::Object options = info[1].As<Napi::Object>();
if (options.Has("seed")) {
context_params.seed = options.Get("seed").As<Napi::Number>().Int32Value();
}
if (options.Has("contextSize")) {
context_params.n_ctx = options.Get("contextSize").As<Napi::Number>().Int32Value();
}
if (options.Has("batchSize")) {
context_params.n_batch = options.Get("batchSize").As<Napi::Number>().Int32Value();
}
if (options.Has("logitsAll")) {
context_params.logits_all = options.Get("logitsAll").As<Napi::Boolean>().Value();
}
if (options.Has("embedding")) {
context_params.embeddings = options.Get("embedding").As<Napi::Boolean>().Value();
}
if (options.Has("threads")) {
context_params.n_threads = options.Get("threads").As<Napi::Number>().Int32Value();
context_params.n_threads_batch == -1 ? context_params.n_threads : context_params.n_threads_batch;
}
}
ctx = llama_new_context_with_model(model->model, context_params);
Napi::MemoryManagement::AdjustExternalMemory(Env(), llama_state_get_size(ctx));
}
~LLAMAContext() {
Napi::MemoryManagement::AdjustExternalMemory(Env(), -(int64_t)llama_state_get_size(ctx));
llama_free(ctx);
model->Unref();
}
Napi::Value Encode(const Napi::CallbackInfo& info) {
std::string text = info[0].As<Napi::String>().Utf8Value();
std::vector<llama_token> tokens = llama_tokenize(ctx, text, false);
Napi::Uint32Array result = Napi::Uint32Array::New(info.Env(), tokens.size());
for (size_t i = 0; i < tokens.size(); ++i) { result[i] = static_cast<uint32_t>(tokens[i]); }
return result;
}
Napi::Value Decode(const Napi::CallbackInfo& info) {
Napi::Uint32Array tokens = info[0].As<Napi::Uint32Array>();
// Create a stringstream for accumulating the decoded string.
std::stringstream ss;
// Decode each token and accumulate the result.
for (size_t i = 0; i < tokens.ElementLength(); i++) {
const std::string piece = llama_token_to_piece(ctx, (llama_token)tokens[i]);
if (piece.empty()) {
continue;
}
ss << piece;
}
return Napi::String::New(info.Env(), ss.str());
}
Napi::Value TokenBos(const Napi::CallbackInfo& info) {
return Napi::Number::From(info.Env(), llama_token_bos(model->model)); // TODO: move this to the model
}
Napi::Value TokenEos(const Napi::CallbackInfo& info) {
return Napi::Number::From(info.Env(), llama_token_eos(model->model)); // TODO: move this to the model
}
Napi::Value TokenNl(const Napi::CallbackInfo& info) {
return Napi::Number::From(info.Env(), llama_token_nl(model->model)); // TODO: move this to the model
}
Napi::Value GetContextSize(const Napi::CallbackInfo& info) {
return Napi::Number::From(info.Env(), llama_n_ctx(ctx));
}
Napi::Value PrintTimings(const Napi::CallbackInfo& info) {
llama_print_timings(ctx);
llama_reset_timings(ctx);
return info.Env().Undefined();
}
Napi::Value GetTokenString(const Napi::CallbackInfo& info) {
int token = info[0].As<Napi::Number>().Int32Value();
std::stringstream ss;
const char* str = llama_token_get_text(model->model, token); // TODO: move this to the model
if (str == nullptr) {
return info.Env().Undefined();
}
ss << str;
return Napi::String::New(info.Env(), ss.str());
}
Napi::Value Eval(const Napi::CallbackInfo& info);
static void init(Napi::Object exports) {
exports.Set("LLAMAContext",
DefineClass(exports.Env(),
"LLAMAContext",
{
InstanceMethod("encode", &LLAMAContext::Encode),
InstanceMethod("decode", &LLAMAContext::Decode),
InstanceMethod("tokenBos", &LLAMAContext::TokenBos),
InstanceMethod("tokenEos", &LLAMAContext::TokenEos),
InstanceMethod("tokenNl", &LLAMAContext::TokenNl),
InstanceMethod("getContextSize", &LLAMAContext::GetContextSize),
InstanceMethod("getTokenString", &LLAMAContext::GetTokenString),
InstanceMethod("eval", &LLAMAContext::Eval),
InstanceMethod("printTimings", &LLAMAContext::PrintTimings),
}));
}
};
class LLAMAContextEvalWorker : Napi::AsyncWorker, Napi::Promise::Deferred {
LLAMAContext* ctx;
LLAMAGrammarEvaluationState* grammar_evaluation_state;
bool use_grammar = false;
std::vector<llama_token> tokens;
llama_token result;
float temperature;
int32_t top_k;
float top_p;
float repeat_penalty = 1.10f; // 1.0 = disabled
float repeat_penalty_presence_penalty = 0.00f; // 0.0 = disabled
float repeat_penalty_frequency_penalty = 0.00f; // 0.0 = disabled
std::vector<llama_token> repeat_penalty_tokens;
bool use_repeat_penalty = false;
public:
LLAMAContextEvalWorker(const Napi::CallbackInfo& info, LLAMAContext* ctx) : Napi::AsyncWorker(info.Env(), "LLAMAContextEvalWorker"), ctx(ctx), Napi::Promise::Deferred(info.Env()) {
ctx->Ref();
Napi::Uint32Array tokens = info[0].As<Napi::Uint32Array>();
temperature = 0.0f;
top_k = 40;
top_p = 0.95f;
if (info.Length() > 1 && info[1].IsObject()) {
Napi::Object options = info[1].As<Napi::Object>();
if (options.Has("temperature")) {
temperature = options.Get("temperature").As<Napi::Number>().FloatValue();
}
if (options.Has("topK")) {
top_k = options.Get("topK").As<Napi::Number>().Int32Value();
}
if (options.Has("topP")) {
top_p = options.Get("topP").As<Napi::Number>().FloatValue();
}
if (options.Has("repeatPenalty")) {
repeat_penalty = options.Get("repeatPenalty").As<Napi::Number>().FloatValue();
}
if (options.Has("repeatPenaltyTokens")) {
Napi::Uint32Array repeat_penalty_tokens_uint32_array = options.Get("repeatPenaltyTokens").As<Napi::Uint32Array>();
repeat_penalty_tokens.reserve(repeat_penalty_tokens_uint32_array.ElementLength());
for (size_t i = 0; i < repeat_penalty_tokens_uint32_array.ElementLength(); i++) {
repeat_penalty_tokens.push_back(static_cast<llama_token>(repeat_penalty_tokens_uint32_array[i]));
}
use_repeat_penalty = true;
}
if (options.Has("repeatPenaltyPresencePenalty")) {
repeat_penalty_presence_penalty = options.Get("repeatPenaltyPresencePenalty").As<Napi::Number>().FloatValue();
}
if (options.Has("repeatPenaltyFrequencyPenalty")) {
repeat_penalty_frequency_penalty = options.Get("repeatPenaltyFrequencyPenalty").As<Napi::Number>().FloatValue();
}
if (options.Has("grammarEvaluationState")) {
grammar_evaluation_state = Napi::ObjectWrap<LLAMAGrammarEvaluationState>::Unwrap(options.Get("grammarEvaluationState").As<Napi::Object>());
grammar_evaluation_state->Ref();
use_grammar = true;
}
}
this->tokens.reserve(tokens.ElementLength());
for (size_t i = 0; i < tokens.ElementLength(); i++) { this->tokens.push_back(static_cast<llama_token>(tokens[i])); }
}
~LLAMAContextEvalWorker() {
ctx->Unref();
if (use_grammar) {
grammar_evaluation_state->Unref();
use_grammar = false;
}
}
using Napi::AsyncWorker::Queue;
using Napi::Promise::Deferred::Promise;
protected:
void Execute() {
llama_batch batch = llama_batch_init(tokens.size(), 0, 1);
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], ctx->n_cur, { 0 }, false);
ctx->n_cur++;
}
GGML_ASSERT(batch.n_tokens == (int) tokens.size());
batch.logits[batch.n_tokens - 1] = true;
// Perform the evaluation using llama_decode.
int r = llama_decode(ctx->ctx, batch);
llama_batch_free(batch);
if (r != 0) {
if (r == 1) {
SetError("could not find a KV slot for the batch (try reducing the size of the batch or increase the context)");
} else {
SetError("Eval has failed");
}
return;
}
llama_token new_token_id = 0;
// Select the best prediction.
auto logits = llama_get_logits_ith(ctx->ctx, batch.n_tokens - 1);
auto n_vocab = llama_n_vocab(ctx->model->model);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto eos_token = llama_token_eos(ctx->model->model);
if (use_repeat_penalty && !repeat_penalty_tokens.empty()) {
llama_sample_repetition_penalties(
ctx->ctx, &candidates_p, repeat_penalty_tokens.data(), repeat_penalty_tokens.size(), repeat_penalty,
repeat_penalty_frequency_penalty, repeat_penalty_presence_penalty
);
}
if (use_grammar && (grammar_evaluation_state)->grammar != nullptr) {
llama_sample_grammar(ctx->ctx, &candidates_p, (grammar_evaluation_state)->grammar);
}
if (temperature <= 0) {
new_token_id = llama_sample_token_greedy(ctx->ctx , &candidates_p);
} else {
const int32_t resolved_top_k = top_k <= 0 ? llama_n_vocab(ctx->model->model) : std::min(top_k, llama_n_vocab(ctx->model->model));
const int32_t n_probs = 0; // Number of probabilities to keep - 0 = disabled
const float tfs_z = 1.00f; // Tail free sampling - 1.0 = disabled
const float typical_p = 1.00f; // Typical probability - 1.0 = disabled
const float resolved_top_p = top_p; // Top p sampling - 1.0 = disabled
// Temperature sampling
size_t min_keep = std::max(1, n_probs);
llama_sample_top_k(ctx->ctx, &candidates_p, resolved_top_k, min_keep);
llama_sample_tail_free(ctx->ctx, &candidates_p, tfs_z, min_keep);
llama_sample_typical(ctx->ctx, &candidates_p, typical_p, min_keep);
llama_sample_top_p(ctx->ctx, &candidates_p, resolved_top_p, min_keep);
llama_sample_temp(ctx->ctx, &candidates_p, temperature);
new_token_id = llama_sample_token(ctx->ctx, &candidates_p);
}
if (new_token_id != eos_token && use_grammar && (grammar_evaluation_state)->grammar != nullptr) {
llama_grammar_accept_token(ctx->ctx, (grammar_evaluation_state)->grammar, new_token_id);
}
result = new_token_id;
}
void OnOK() {
Napi::Env env = Napi::AsyncWorker::Env();
Napi::Number resultValue = Napi::Number::New(env, static_cast<uint32_t>(result));
Napi::Promise::Deferred::Resolve(resultValue);
}
void OnError(const Napi::Error& err) { Napi::Promise::Deferred::Reject(err.Value()); }
};
Napi::Value LLAMAContext::Eval(const Napi::CallbackInfo& info) {
LLAMAContextEvalWorker* worker = new LLAMAContextEvalWorker(info, this);
worker->Queue();
return worker->Promise();
}
Napi::Value systemInfo(const Napi::CallbackInfo& info) { return Napi::String::From(info.Env(), llama_print_system_info()); }
Napi::Object registerCallback(Napi::Env env, Napi::Object exports) {
llama_backend_init();
exports.DefineProperties({
Napi::PropertyDescriptor::Function("systemInfo", systemInfo),
});
LLAMAModel::init(exports);
LLAMAGrammar::init(exports);
LLAMAGrammarEvaluationState::init(exports);
LLAMAContext::init(exports);
return exports;
}
NODE_API_MODULE(NODE_GYP_MODULE_NAME, registerCallback)