From 55084333a93b226ac6c3538471970eba7e584211 Mon Sep 17 00:00:00 2001 From: Adam Treat Date: Sat, 15 Apr 2023 15:57:32 -0400 Subject: [PATCH] Add llama.cpp support for loading llama based models in the gui. We now support loading both gptj derived models and llama derived models. --- .gitmodules | 3 + CMakeLists.txt | 10 +- gptj.cpp | 8 +- gptj.h | 1 + llama.cpp | 1 + llamamodel.cpp | 160 ++++++++++++++++++++++++++++++ llamamodel.h | 28 ++++++ llm.cpp | 18 +++- llm.h | 1 + llmodel.h | 5 +- main.qml | 8 +- utils.cpp | 257 +++++++++++++++++++++++++++++++++++++++++++++++++ utils.h | 83 ++++++++++++++++ 13 files changed, 571 insertions(+), 12 deletions(-) create mode 160000 llama.cpp create mode 100644 llamamodel.cpp create mode 100644 llamamodel.h create mode 100644 utils.cpp create mode 100644 utils.h diff --git a/.gitmodules b/.gitmodules index 1a30094e..a81ef356 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,6 @@ [submodule "ggml"] path = ggml url = https://github.com/manyoso/ggml.git +[submodule "llama.cpp"] + path = llama.cpp + url = https://github.com/manyoso/llama.cpp.git diff --git a/CMakeLists.txt b/CMakeLists.txt index 793a9926..000fd162 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -28,15 +28,19 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON) find_package(Qt6 6.2 COMPONENTS Quick Svg REQUIRED) -set(GGML_BUILD_EXAMPLES ON CACHE BOOL "ggml: build examples" FORCE) -add_subdirectory(ggml) +set(LLAMA_BUILD_EXAMPLES ON CACHE BOOL "llama: build examples" FORCE) +set(BUILD_SHARED_LIBS ON FORCE) +add_subdirectory(llama.cpp) qt_add_executable(chat main.cpp download.h download.cpp gptj.h gptj.cpp + llamamodel.h llamamodel.cpp + llama.cpp/examples/common.cpp llm.h llm.cpp llmodel.h + utils.h utils.cpp ) qt_add_qml_module(chat @@ -72,7 +76,7 @@ target_compile_definitions(chat target_link_libraries(chat PRIVATE Qt6::Quick Qt6::Svg) target_link_libraries(chat - PRIVATE ggml ggml_utils) + PRIVATE llama) set(COMPONENT_NAME_MAIN ${PROJECT_NAME}) set(CMAKE_INSTALL_PREFIX ${CMAKE_BINARY_DIR}/install) diff --git a/gptj.cpp b/gptj.cpp index 0e40a4a1..cf55d5cc 100644 --- a/gptj.cpp +++ b/gptj.cpp @@ -1,5 +1,5 @@ #include "gptj.h" -#include "ggml/ggml.h" +#include "llama.cpp/ggml.h" #include "utils.h" @@ -644,6 +644,12 @@ GPTJ::GPTJ() d_ptr->modelLoaded = false; } +bool GPTJ::loadModel(const std::string &modelPath) +{ + std::cerr << "GPTJ ERROR: loading gpt model from file unsupported!\n"; + return false; +} + bool GPTJ::loadModel(const std::string &modelPath, std::istream &fin) { std::mt19937 rng(time(NULL)); d_ptr->rng = rng; diff --git a/gptj.h b/gptj.h index 59c0a79c..72fc4109 100644 --- a/gptj.h +++ b/gptj.h @@ -12,6 +12,7 @@ public: GPTJ(); ~GPTJ(); + bool loadModel(const std::string &modelPath) override; bool loadModel(const std::string &modelPath, std::istream &fin) override; bool isModelLoaded() const override; void prompt(const std::string &prompt, std::function response, diff --git a/llama.cpp b/llama.cpp new file mode 160000 index 00000000..c8c2c524 --- /dev/null +++ b/llama.cpp @@ -0,0 +1 @@ +Subproject commit c8c2c524827be8fd681a63f0e5a697b0bf4c587b diff --git a/llamamodel.cpp b/llamamodel.cpp new file mode 100644 index 00000000..5ac656e1 --- /dev/null +++ b/llamamodel.cpp @@ -0,0 +1,160 @@ +#include "llamamodel.h" + +#include "llama.cpp/examples/common.h" +#include "llama.cpp/llama.h" +#include "llama.cpp/ggml.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +struct LLamaPrivate { + const std::string modelPath; + bool modelLoaded; + llama_context *ctx = nullptr; + llama_context_params params; + int64_t n_threads = 0; +}; + +LLamaModel::LLamaModel() + : d_ptr(new LLamaPrivate) { + + d_ptr->modelLoaded = false; +} + +bool LLamaModel::loadModel(const std::string &modelPath, std::istream &fin) +{ + std::cerr << "LLAMA ERROR: loading llama model from stream unsupported!\n"; + return false; +} + +bool LLamaModel::loadModel(const std::string &modelPath) +{ + // load the model + d_ptr->params = llama_context_default_params(); + d_ptr->ctx = llama_init_from_file(modelPath.c_str(), d_ptr->params); + if (!d_ptr->ctx) { + std::cerr << "LLAMA ERROR: failed to load model from " << modelPath << std::endl; + return false; + } + + d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); + d_ptr->modelLoaded = true; + return true; +} + +void LLamaModel::setThreadCount(int32_t n_threads) { + d_ptr->n_threads = n_threads; +} + +int32_t LLamaModel::threadCount() { + return d_ptr->n_threads; +} + +LLamaModel::~LLamaModel() +{ +} + +bool LLamaModel::isModelLoaded() const +{ + return d_ptr->modelLoaded; +} + +void LLamaModel::prompt(const std::string &prompt, std::function response, + PromptContext &promptCtx, int32_t n_predict, int32_t top_k, float top_p, float temp, int32_t n_batch) { + + if (!isModelLoaded()) { + std::cerr << "LLAMA ERROR: prompt won't work with an unloaded model!\n"; + return; + } + + gpt_params params; + params.prompt = prompt; + + // Add a space in front of the first character to match OG llama tokenizer behavior + params.prompt.insert(0, 1, ' '); + + // tokenize the prompt + auto embd_inp = ::llama_tokenize(d_ptr->ctx, params.prompt, false); + const int n_ctx = llama_n_ctx(d_ptr->ctx); + + if ((int) embd_inp.size() > n_ctx - 4) { + std::cerr << "LLAMA ERROR: prompt is too long\n"; + return; + } + + n_predict = std::min(n_predict, n_ctx - (int) embd_inp.size()); + promptCtx.n_past = std::min(promptCtx.n_past, n_ctx); + + // number of tokens to keep when resetting context + params.n_keep = (int)embd_inp.size(); + + // process the prompt in batches + size_t i = 0; + const int64_t t_start_prompt_us = ggml_time_us(); + while (i < embd_inp.size()) { + size_t batch_end = std::min(i + n_batch, embd_inp.size()); + std::vector batch(embd_inp.begin() + i, embd_inp.begin() + batch_end); + + if (promptCtx.n_past + batch.size() > n_ctx) { + std::cerr << "eval n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl; + promptCtx.n_past = std::min(promptCtx.n_past, int(n_ctx - batch.size())); + std::cerr << "after n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl; + } + + if (llama_eval(d_ptr->ctx, batch.data(), batch.size(), promptCtx.n_past, d_ptr->n_threads)) { + std::cerr << "LLAMA ERROR: Failed to process prompt\n"; + return; + } + // We pass a null string for each token to see if the user has asked us to stop... + size_t tokens = batch_end - i; + for (size_t t = 0; t < tokens; ++t) + if (!response("")) + return; + promptCtx.n_past += batch.size(); + i = batch_end; + } + + std::vector cachedTokens; + + // predict next tokens + int32_t totalPredictions = 0; + for (int i = 0; i < n_predict; i++) { + // sample next token + llama_token id = llama_sample_top_p_top_k(d_ptr->ctx, {}, 0, top_k, top_p, temp, 1.0f); + + if (promptCtx.n_past + 1 > n_ctx) { + std::cerr << "eval 2 n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl; + promptCtx.n_past = std::min(promptCtx.n_past, n_ctx - 1); + std::cerr << "after 2 n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl; + } + + if (llama_eval(d_ptr->ctx, &id, 1, promptCtx.n_past, d_ptr->n_threads)) { + std::cerr << "LLAMA ERROR: Failed to predict next token\n"; + return; + } + cachedTokens.emplace_back(id); + + for (int j = 0; j < cachedTokens.size(); ++j) { + llama_token cachedToken = cachedTokens.at(j); + promptCtx.n_past += 1; + // display text + ++totalPredictions; + if (id == llama_token_eos() || !response(llama_token_to_str(d_ptr->ctx, cachedToken))) + goto stop_generating; + } + cachedTokens.clear(); + } + +stop_generating: + return; +} diff --git a/llamamodel.h b/llamamodel.h new file mode 100644 index 00000000..9ed73d6d --- /dev/null +++ b/llamamodel.h @@ -0,0 +1,28 @@ +#ifndef LLAMAMODEL_H +#define LLAMAMODEL_H + +#include +#include +#include +#include "llmodel.h" + +class LLamaPrivate; +class LLamaModel : public LLModel { +public: + LLamaModel(); + ~LLamaModel(); + + bool loadModel(const std::string &modelPath) override; + bool loadModel(const std::string &modelPath, std::istream &fin) override; + bool isModelLoaded() const override; + void prompt(const std::string &prompt, std::function response, + PromptContext &ctx, int32_t n_predict = 200, int32_t top_k = 50400, float top_p = 1.0f, + float temp = 0.0f, int32_t n_batch = 9) override; + void setThreadCount(int32_t n_threads) override; + int32_t threadCount() override; + +private: + LLamaPrivate *d_ptr; +}; + +#endif // LLAMAMODEL_H \ No newline at end of file diff --git a/llm.cpp b/llm.cpp index d0bb85d5..9c175785 100644 --- a/llm.cpp +++ b/llm.cpp @@ -47,20 +47,32 @@ bool LLMObject::loadModelPrivate(const QString &modelName) return true; if (isModelLoaded()) { + resetContext(); delete m_llmodel; m_llmodel = nullptr; emit isModelLoadedChanged(); } - m_llmodel = new GPTJ; - + bool isGPTJ = false; QString filePath = QCoreApplication::applicationDirPath() + QDir::separator() + "ggml-" + modelName + ".bin"; QFileInfo info(filePath); if (info.exists()) { auto fin = std::ifstream(filePath.toStdString(), std::ios::binary); - m_llmodel->loadModel(modelName.toStdString(), fin); + + uint32_t magic; + fin.read((char *) &magic, sizeof(magic)); + fin.seekg(0); + isGPTJ = magic == 0x67676d6c; + if (isGPTJ) { + m_llmodel = new GPTJ; + m_llmodel->loadModel(modelName.toStdString(), fin); + } else { + m_llmodel = new LLamaModel; + m_llmodel->loadModel(filePath.toStdString()); + } + emit isModelLoadedChanged(); emit threadCountChanged(); } diff --git a/llm.h b/llm.h index 2c54e634..27bf3dcb 100644 --- a/llm.h +++ b/llm.h @@ -4,6 +4,7 @@ #include #include #include "gptj.h" +#include "llamamodel.h" class LLMObject : public QObject { diff --git a/llmodel.h b/llmodel.h index 3ffb8420..829e4145 100644 --- a/llmodel.h +++ b/llmodel.h @@ -10,6 +10,7 @@ public: explicit LLModel() {} virtual ~LLModel() {} + virtual bool loadModel(const std::string &modelPath) = 0; virtual bool loadModel(const std::string &modelPath, std::istream &fin) = 0; virtual bool isModelLoaded() const = 0; struct PromptContext { @@ -19,8 +20,8 @@ public: virtual void prompt(const std::string &prompt, std::function response, PromptContext &ctx, int32_t n_predict = 200, int32_t top_k = 40, float top_p = 0.9f, float temp = 0.9f, int32_t n_batch = 9) = 0; - virtual void setThreadCount(int32_t n_threads); - virtual int32_t threadCount(); + virtual void setThreadCount(int32_t n_threads) {} + virtual int32_t threadCount() { return 1; } }; #endif // LLMODEL_H diff --git a/main.qml b/main.qml index e677db93..803390d9 100644 --- a/main.qml +++ b/main.qml @@ -70,7 +70,9 @@ Window { } onActivated: { + LLM.stopGenerating() LLM.modelName = comboBox.currentText + chatModel.clear() } } } @@ -775,7 +777,7 @@ Window { Accessible.description: qsTr("This is the list of prompt/response pairs comprising the actual conversation with the model") delegate: TextArea { - text: currentResponse ? LLM.response : value + text: currentResponse ? LLM.response : (value ? value : "") width: listView.width color: "#d1d5db" wrapMode: Text.WordWrap @@ -800,8 +802,8 @@ Window { anchors.leftMargin: 90 anchors.top: parent.top anchors.topMargin: 5 - visible: currentResponse && LLM.response === "" && LLM.responseInProgress - running: currentResponse && LLM.response === "" && LLM.responseInProgress + visible: (currentResponse ? true : false) && LLM.response === "" && LLM.responseInProgress + running: (currentResponse ? true : false) && LLM.response === "" && LLM.responseInProgress Accessible.role: Accessible.Animation Accessible.name: qsTr("Busy indicator") diff --git a/utils.cpp b/utils.cpp new file mode 100644 index 00000000..a77fb7a3 --- /dev/null +++ b/utils.cpp @@ -0,0 +1,257 @@ +#include "utils.h" + +#include +#include + +void replace(std::string & str, const std::string & needle, const std::string & replacement) { + size_t pos = 0; + while ((pos = str.find(needle, pos)) != std::string::npos) { + str.replace(pos, needle.length(), replacement); + pos += replacement.length(); + } +} + +std::map json_parse(const std::string & fname) { + std::map result; + + // read file into string + std::string json; + { + std::ifstream ifs(fname); + if (!ifs) { + fprintf(stderr, "Failed to open %s\n", fname.c_str()); + exit(1); + } + + json = std::string((std::istreambuf_iterator(ifs)), + (std::istreambuf_iterator())); + } + + if (json[0] != '{') { + return result; + } + + // parse json + { + bool has_key = false; + bool in_token = false; + + std::string str_key = ""; + std::string str_val = ""; + + int n = json.size(); + for (int i = 1; i < n; ++i) { + if (!in_token) { + if (json[i] == ' ') continue; + if (json[i] == '"') { + in_token = true; + continue; + } + } else { + if (json[i] == '\\' && i+1 < n) { + if (has_key == false) { + str_key += json[i]; + } else { + str_val += json[i]; + } + ++i; + } else if (json[i] == '"') { + if (has_key == false) { + has_key = true; + ++i; + while (json[i] == ' ') ++i; + ++i; // : + while (json[i] == ' ') ++i; + if (json[i] != '\"') { + while (json[i] != ',' && json[i] != '}') { + str_val += json[i++]; + } + has_key = false; + } else { + in_token = true; + continue; + } + } else { + has_key = false; + } + + ::replace(str_key, "\\u0120", " " ); // \u0120 -> space + ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line + ::replace(str_key, "\\\"", "\""); // \\\" -> " + + try { + result[str_key] = std::stoi(str_val); + } catch (...) { + //fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str()); + + } + str_key = ""; + str_val = ""; + in_token = false; + continue; + } + if (has_key == false) { + str_key += json[i]; + } else { + str_val += json[i]; + } + } + } + } + + return result; +} + +std::vector gpt_tokenize(const gpt_vocab & vocab, const std::string & text) { + std::vector words; + + // first split the text into words + { + std::string str = text; + std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"; + + std::regex re(pat); + std::smatch m; + + while (std::regex_search(str, m, re)) { + for (auto x : m) { + words.push_back(x); + } + str = m.suffix(); + } + } + + // find the longest tokens that form the words: + std::vector tokens; + for (const auto & word : words) { + if (word.size() == 0) continue; + + int i = 0; + int n = word.size(); + while (i < n) { + int j = n; + while (j > i) { + auto it = vocab.token_to_id.find(word.substr(i, j-i)); + if (it != vocab.token_to_id.end()) { + tokens.push_back(it->second); + i = j; + break; + } + --j; + } + if (i == n) { + break; + } + if (j == i) { + auto sub = word.substr(i, 1); + if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) { + tokens.push_back(vocab.token_to_id.at(sub)); + } else { + fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data()); + } + ++i; + } + } + } + + return tokens; +} + +bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) { + printf("%s: loading vocab from '%s'\n", __func__, fname.c_str()); + + vocab.token_to_id = ::json_parse(fname); + + for (const auto & kv : vocab.token_to_id) { + vocab.id_to_token[kv.second] = kv.first; + } + + printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size()); + + // print the vocabulary + //for (auto kv : vocab.token_to_id) { + // printf("'%s' -> %d\n", kv.first.data(), kv.second); + //} + + return true; +} + +gpt_vocab::id gpt_sample_top_k_top_p( + const gpt_vocab & vocab, + const float * logits, + int top_k, + double top_p, + double temp, + std::mt19937 & rng) { + int n_logits = vocab.id_to_token.size(); + + std::vector> logits_id; + logits_id.reserve(n_logits); + + { + const double scale = 1.0/temp; + for (int i = 0; i < n_logits; ++i) { + logits_id.push_back(std::make_pair(logits[i]*scale, i)); + } + } + + // find the top K tokens + std::partial_sort( + logits_id.begin(), + logits_id.begin() + top_k, logits_id.end(), + [](const std::pair & a, const std::pair & b) { + return a.first > b.first; + }); + + logits_id.resize(top_k); + + double maxl = -INFINITY; + for (const auto & kv : logits_id) { + maxl = std::max(maxl, kv.first); + } + + // compute probs for the top K tokens + std::vector probs; + probs.reserve(logits_id.size()); + + double sum = 0.0; + for (const auto & kv : logits_id) { + double p = exp(kv.first - maxl); + probs.push_back(p); + sum += p; + } + + // normalize the probs + for (auto & p : probs) { + p /= sum; + } + + if (top_p < 1.0f) { + double cumsum = 0.0f; + for (int i = 0; i < top_k; i++) { + cumsum += probs[i]; + if (cumsum >= top_p) { + top_k = i + 1; + probs.resize(top_k); + logits_id.resize(top_k); + break; + } + } + + cumsum = 1.0/cumsum; + for (int i = 0; i < (int) probs.size(); i++) { + probs[i] *= cumsum; + } + } + + //printf("\n"); + //for (int i = 0; i < (int) probs.size(); i++) { + // printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]); + //} + //exit(0); + + std::discrete_distribution<> dist(probs.begin(), probs.end()); + int idx = dist(rng); + + return logits_id[idx].second; +} diff --git a/utils.h b/utils.h new file mode 100644 index 00000000..b61173ff --- /dev/null +++ b/utils.h @@ -0,0 +1,83 @@ +// Various helper functions and utilities + +#pragma once + +#include +#include +#include +#include +#include + +// +// CLI argument parsing +// + +struct gpt_params { + int32_t seed = -1; // RNG seed + int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); + int32_t n_predict = 200; // new tokens to predict + + // sampling parameters + int32_t top_k = 40; + float top_p = 0.9f; + float temp = 0.9f; + + int32_t n_batch = 8; // batch size for prompt processing + + std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path + std::string prompt; +}; + +bool gpt_params_parse(int argc, char ** argv, gpt_params & params); + +void gpt_print_usage(int argc, char ** argv, const gpt_params & params); + +std::string gpt_random_prompt(std::mt19937 & rng); + +// +// Vocab utils +// + +struct gpt_vocab { + using id = int32_t; + using token = std::string; + + std::map token_to_id; + std::map id_to_token; +}; + +void replace(std::string & str, const std::string & needle, const std::string & replacement); + +// poor-man's JSON parsing +std::map json_parse(const std::string & fname); + +// split text into tokens +// +// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53 +// +// Regex (Python): +// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" +// +// Regex (C++): +// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)" +// +std::vector gpt_tokenize(const gpt_vocab & vocab, const std::string & text); + +// load the tokens from encoder.json +bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab); + +// sample next token given probabilities for each embedding +// +// - consider only the top K tokens +// - from them, consider only the top tokens with cumulative probability > P +// +// TODO: not sure if this implementation is correct +// TODO: temperature is not implemented +// +gpt_vocab::id gpt_sample_top_k_top_p( + const gpt_vocab & vocab, + const float * logits, + int top_k, + double top_p, + double temp, + std::mt19937 & rng);