Patches for llama.cpp and repeng to run this stuff.

patches/README.md

@@ -0,0 +1,6 @@
+Assumption is that you have llama.cpp and repeng directories under the same parent directory.
+Copy files from patches/repeng into your repeng diretory.
+Apply the diff in patches/llama.cpp inside your llama.cpp directory.
+Place trainvector.sh in their common parent directory.
+Run ./trainvector.sh {name-of-your-vector} to generate a vector based on the prompts in your edited repeng/emotion_prompts.py
+

patches/llama/README.md

@@ -0,0 +1,2 @@
+werks on top of llama.cpp commit c47cf414efafb8f60596edc7edb5a2d68065e992
+

patches/llama/llama-repeng.diff

@@ -0,0 +1,855 @@
+Parent: a56d09a4407f29c21e149b44fd5308f83aa1cb09
+Author: Anon <anon>
+Date:   Tue Mar 19 02:46:47 2024 +0000
+
+    repeng: implement batching
+diff --git a/Makefile b/Makefile
+index c0f12503..d471c387 100644
+--- a/Makefile
++++ b/Makefile
+@@ -1,6 +1,6 @@
+ # Define the default target now so that it is always the first target
+ BUILD_TARGETS = \
+-	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
++	main repeng quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+ 	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
+ 	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
+ 
+@@ -744,6 +744,13 @@ server: examples/server/server.cpp examples/server/utils.hpp examples/server/htt
+ 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+ 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+ 
++repeng: examples/repeng/repeng.cpp                                  ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
++       $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
++       $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
++       @echo
++       @echo '====  Run ./repeng -h for help.  ===='
++       @echo
++
+ gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
+ 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+ 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
+index e762cf8b..d46d9d17 100644
+--- a/examples/CMakeLists.txt
++++ b/examples/CMakeLists.txt
+@@ -46,4 +46,5 @@ else()
+         add_subdirectory(server)
+     endif()
+     add_subdirectory(export-lora)
++    add_subdirectory(repeng)
+ endif()
+diff --git a/examples/repeng/CMakeLists.txt b/examples/repeng/CMakeLists.txt
+new file mode 100644
+index 00000000..9e20f806
+--- /dev/null
++++ b/examples/repeng/CMakeLists.txt
+@@ -0,0 +1,5 @@
++set(TARGET repeng)
++add_executable(${TARGET} repeng.cpp)
++install(TARGETS ${TARGET} RUNTIME)
++target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
++target_compile_features(${TARGET} PRIVATE cxx_std_11)
+diff --git a/examples/repeng/repeng.cpp b/examples/repeng/repeng.cpp
+new file mode 100644
+index 00000000..5863c8be
+--- /dev/null
++++ b/examples/repeng/repeng.cpp
+@@ -0,0 +1,797 @@
++#include "common.h"
++
++#include "console.h"
++#include "llama.h"
++
++#include <cassert>
++#include <cinttypes>
++#include <cmath>
++#include <cstdio>
++#include <cstring>
++#include <ctime>
++#include <fstream>
++#include <iostream>
++#include <sstream>
++#include <string>
++#include <vector>
++
++#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
++#include <signal.h>
++#include <unistd.h>
++#elif defined (_WIN32)
++#define WIN32_LEAN_AND_MEAN
++#ifndef NOMINMAX
++#define NOMINMAX
++#endif
++#include <windows.h>
++#include <signal.h>
++#endif
++
++#if defined(_MSC_VER)
++#pragma warning(disable: 4244 4267) // possible loss of data
++#endif
++
++static llama_context           ** g_ctx;
++static llama_model             ** g_model;
++static gpt_params               * g_params;
++static std::vector<llama_token> * g_input_tokens;
++static std::ostringstream       * g_output_ss;
++static std::vector<llama_token> * g_output_tokens;
++static bool is_interacting = false;
++
++static bool file_exists(const std::string &path) {
++    std::ifstream f(path.c_str());
++    return f.good();
++}
++
++static bool file_is_empty(const std::string &path) {
++    std::ifstream f;
++    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
++    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
++    return f.tellg() == 0;
++}
++
++#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
++static void sigint_handler(int signo) {
++    if (signo == SIGINT) {
++        if (!is_interacting && g_params->interactive) {
++            is_interacting = true;
++        } else {
++            console::cleanup();
++            printf("\n");
++            llama_print_timings(*g_ctx);
++            //write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
++            _exit(130);
++        }
++    }
++}
++#endif
++
++static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
++    (void) level;
++    (void) user_data;
++    LOG_TEE("%s", text);
++}
++
++static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval(
++    gpt_params & params,
++    ggml_backend_sched_eval_callback cb_eval,
++    void * cb_eval_user_data) {
++    auto mparams = llama_model_params_from_gpt_params(params);
++
++    llama_model * model  = llama_load_model_from_file(params.model.c_str(), mparams);
++    if (model == NULL) {
++        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
++        return std::make_tuple(nullptr, nullptr);
++    }
++
++    auto cparams = llama_context_params_from_gpt_params(params);
++
++    cparams.cb_eval = cb_eval;
++    cparams.cb_eval_user_data = cb_eval_user_data;
++
++    llama_context * lctx = llama_new_context_with_model(model, cparams);
++    if (lctx == NULL) {
++        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
++        llama_free_model(model);
++        return std::make_tuple(nullptr, nullptr);
++    }
++
++    if (!params.control_vectors.empty()) {
++        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
++        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
++
++        const auto cvec = llama_control_vector_load(params.control_vectors);
++        if (cvec.n_embd == -1) {
++            llama_free(lctx);
++            llama_free_model(model);
++            return std::make_tuple(nullptr, nullptr);
++        }
++
++        int err = llama_control_vector_apply(lctx,
++                                             cvec.data.data(),
++                                             cvec.data.size(),
++                                             cvec.n_embd,
++                                             params.control_vector_layer_start,
++                                             params.control_vector_layer_end);
++        if (err) {
++            llama_free(lctx);
++            llama_free_model(model);
++            return std::make_tuple(nullptr, nullptr);
++        }
++    }
++
++    for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
++        const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
++        float lora_scale = std::get<1>(params.lora_adapter[i]);
++        int err = llama_model_apply_lora_from_file(model,
++                                             lora_adapter.c_str(),
++                                             lora_scale,
++                                             ((i > 0) || params.lora_base.empty())
++                                                ? NULL
++                                                : params.lora_base.c_str(),
++                                             params.n_threads);
++        if (err != 0) {
++            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
++            llama_free(lctx);
++            llama_free_model(model);
++            return std::make_tuple(nullptr, nullptr);
++        }
++    }
++
++    if (params.ignore_eos) {
++        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
++    }
++
++    {
++        LOG("warming up the model with an empty run\n");
++
++        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
++        llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
++        llama_kv_cache_clear(lctx);
++        llama_synchronize(lctx);
++        llama_reset_timings(lctx);
++    }
++
++    return std::make_tuple(model, lctx);
++}
++
++struct eval_callback_state {
++    std::vector<ggml_tensor *> tensors;
++    int first_prompt_idx;
++    std::vector<int> extract_tokens;
++};
++
++static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) {
++    struct eval_callback_state * eval_state = (eval_callback_state *)user_data;
++    if (ask) {
++        // Report whether we want to observe this tensor.
++        if (strncmp(t->name, "l_out-", 6) == 0) {
++            return true;
++        } else {
++            return false;
++        }
++    } else {
++        // Actually observe the tensor data.
++
++        if (eval_state->first_prompt_idx >= 0) {
++            // Find the tensor collecting hidden states for the current layer.
++            ggml_tensor * output_tensor = nullptr;
++            for (auto t2 : eval_state->tensors) {
++                if (strcmp(t2->name, t->name) == 0) {
++                    output_tensor = t2;
++                    break;
++                }
++            }
++
++            if (output_tensor != nullptr) {
++                int output_idx = eval_state->first_prompt_idx;
++                for (int input_idx : eval_state->extract_tokens) {
++                    // Copy the hidden states for the last token into
++                    size_t input_offset = t->nb[1] * input_idx;
++                    size_t output_offset = output_tensor->nb[1] * output_idx;
++                    assert(t->nb[0] == output_tensor->nb[0]);
++                    assert(t->ne[0] == output_tensor->ne[0]);
++                    ggml_backend_tensor_get(t,
++                            (char *)output_tensor->data + output_offset,
++                            input_offset,
++                            t->nb[0] * t->ne[0]);
++                    //memcpy((char *)output_tensor->data + output_offset,
++                    //        (char *)t->data + input_offset,
++                    //        t->nb[0] * t->ne[0]);
++                    //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data "
++                    //    << " for " << t->name << " in slot " << output_idx << "\n";
++
++                    //float * buf = (float *)((char *)t->data + input_offset);
++                    //float * buf = (float *)((char *)output_tensor->data + output_offset);
++                    //std::cerr << "prompt " << output_idx
++                    //    << " tensor contents for " << t->name << ": "
++                    //    << buf[0] << ", "
++                    //    << buf[1] << ", "
++                    //    << buf[2] << " ... "
++                    //    << buf[4093] << ", "
++                    //    << buf[4094] << ", "
++                    //    << buf[4095] << "\n";
++
++                    ++output_idx;
++                }
++            }
++        }
++
++        // Continue running
++        return true;
++    }
++}
++
++int main(int argc, char ** argv) {
++    gpt_params params;
++    g_params = &params;
++
++    if (!gpt_params_parse(argc, argv, params)) {
++        return 1;
++    }
++    llama_sampling_params & sparams = params.sparams;
++
++#ifndef LOG_DISABLE_LOGS
++    log_set_target(log_filename_generator("main", "log"));
++    LOG_TEE("Log start\n");
++    log_dump_cmdline(argc, argv);
++    llama_log_set(llama_log_callback_logTee, nullptr);
++#endif // LOG_DISABLE_LOGS
++
++    // TODO: Dump params ?
++    //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
++
++    // save choice to use color for later
++    // (note for later: this is a slightly awkward choice)
++    console::init(params.simple_io, params.use_color);
++    atexit([]() { console::cleanup(); });
++
++    if (params.logits_all) {
++        printf("\n************\n");
++        printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
++        printf("************\n\n");
++
++        return 0;
++    }
++
++    if (params.embedding) {
++        printf("\n************\n");
++        printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
++        printf("************\n\n");
++
++        return 0;
++    }
++
++    if (params.n_ctx != 0 && params.n_ctx < 8) {
++        LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
++        params.n_ctx = 8;
++    }
++
++    if (params.rope_freq_base != 0.0) {
++        LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
++    }
++
++    if (params.rope_freq_scale != 0.0) {
++        LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
++    }
++
++    LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
++    LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
++
++    if (params.seed == LLAMA_DEFAULT_SEED) {
++        params.seed = time(NULL);
++    }
++
++    LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
++
++    std::mt19937 rng(params.seed);
++    if (params.random_prompt) {
++        params.prompt = gpt_random_prompt(rng);
++    }
++
++    LOG("%s: llama backend init\n", __func__);
++    llama_backend_init();
++    llama_numa_init(params.numa);
++
++    llama_model * model;
++    llama_context * ctx;
++    llama_context * ctx_guidance = NULL;
++    g_model = &model;
++    g_ctx = &ctx;
++
++    ggml_context * eval_ctx = nullptr;
++    struct eval_callback_state eval_state;
++
++    // load the model and apply lora adapter, if any
++    LOG("%s: load the model and apply lora adapter, if any\n", __func__);
++    std::tie(model, ctx) = llama_init_from_gpt_params_with_cb_eval(
++        params,
++        eval_callback,
++        (void *)&eval_state);
++    /*
++    if (sparams.cfg_scale > 1.f) {
++        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
++        ctx_guidance = llama_new_context_with_model(model, lparams);
++    }
++    */
++
++    if (model == NULL) {
++        LOG_TEE("%s: error: unable to load model\n", __func__);
++        return 1;
++    }
++
++    const int n_ctx_train = llama_n_ctx_train(model);
++    const int n_ctx = llama_n_ctx(ctx);
++    LOG("n_ctx: %d\n", n_ctx);
++
++    if (n_ctx > n_ctx_train) {
++        LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n",
++                __func__, n_ctx_train, n_ctx);
++    }
++
++    // print system information
++    {
++        LOG_TEE("\n");
++        LOG_TEE("%s\n", get_system_info(params).c_str());
++    }
++
++    std::string path_session = params.path_prompt_cache;
++    std::vector<llama_token> session_tokens;
++
++    if (!path_session.empty()) {
++        LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
++        if (!file_exists(path_session)) {
++            LOG_TEE("%s: session file does not exist, will create.\n", __func__);
++        } else if (file_is_empty(path_session)) {
++            LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__);
++        } else {
++            // The file exists and is not empty
++            session_tokens.resize(n_ctx);
++            size_t n_token_count_out = 0;
++            if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
++                LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
++                return 1;
++            }
++            session_tokens.resize(n_token_count_out);
++            llama_set_rng_seed(ctx, params.seed);
++            LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
++        }
++    }
++
++    const bool add_bos = llama_should_add_bos_token(model);
++    LOG("add_bos: %d\n", add_bos);
++
++    std::vector<llama_token> embd_inp;
++
++    if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) {
++        LOG("tokenize the prompt\n");
++        if (params.chatml) {
++            params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
++        }
++        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
++    } else {
++        LOG("use session tokens\n");
++        embd_inp = session_tokens;
++    }
++
++    LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
++    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
++
++    // Should not run without any tokens
++    if (embd_inp.empty()) {
++        embd_inp.push_back(llama_token_bos(model));
++        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
++    }
++
++    // Tokenize negative prompt
++    std::vector<llama_token> guidance_inp;
++    int guidance_offset = 0;
++    int original_prompt_len = 0;
++    /*
++    if (ctx_guidance) {
++        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
++
++        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
++        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
++
++        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
++        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
++
++        original_prompt_len = original_inp.size();
++        guidance_offset = (int)guidance_inp.size() - original_prompt_len;
++        LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
++        LOG("guidance_offset:     %s", log_tostr(guidance_offset));
++    }
++    */
++
++    /*
++    if ((int) embd_inp.size() > n_ctx - 4) {
++        LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
++        return 1;
++    }
++    */
++
++    // debug message about similarity of saved session, if applicable
++    size_t n_matching_session_tokens = 0;
++    if (!session_tokens.empty()) {
++        for (llama_token id : session_tokens) {
++            if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
++                break;
++            }
++            n_matching_session_tokens++;
++        }
++        if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
++            LOG_TEE("%s: using full prompt from session file\n", __func__);
++        } else if (n_matching_session_tokens >= embd_inp.size()) {
++            LOG_TEE("%s: session file has exact match for prompt!\n", __func__);
++        } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
++            LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
++                __func__, n_matching_session_tokens, embd_inp.size());
++        } else {
++            LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
++                __func__, n_matching_session_tokens, embd_inp.size());
++        }
++
++        // remove any "future" tokens that we might have inherited from the previous session
++        llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);
++    }
++
++    LOGLN(
++            "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
++            log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());
++
++    // if we will use the cache for the full prompt without reaching the end of the cache, force
++    // reevaluation of the last token token to recalculate the cached logits
++    if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
++        LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);
++
++        session_tokens.resize(embd_inp.size() - 1);
++    }
++
++    // number of tokens to keep when resetting context
++    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
++        params.n_keep = (int)embd_inp.size();
++    } else {
++        params.n_keep += add_bos; // always keep the BOS token
++    }
++
++    // prefix & suffix for instruct mode
++    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
++    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
++
++    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
++    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
++
++    // chatml prefix & suffix
++    const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
++    const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
++
++    LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
++    LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
++
++    // in instruct mode, we inject a prefix and a suffix to each input by the user
++    if (params.instruct) {
++        params.interactive_first = true;
++        params.antiprompt.emplace_back("### Instruction:\n\n");
++    }
++    // similar for chatml mode
++    else if (params.chatml) {
++        params.interactive_first = true;
++        params.antiprompt.emplace_back("<|im_start|>user\n");
++    }
++
++    // enable interactive mode if interactive start is specified
++    if (params.interactive_first) {
++        params.interactive = true;
++    }
++
++    if (params.verbose_prompt) {
++        LOG_TEE("\n");
++        LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
++        LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
++        for (int i = 0; i < (int) embd_inp.size(); i++) {
++            LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
++        }
++
++        /*
++        if (ctx_guidance) {
++            LOG_TEE("\n");
++            LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
++            LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
++            for (int i = 0; i < (int) guidance_inp.size(); i++) {
++                LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
++            }
++        }
++        */
++
++        if (params.n_keep > add_bos) {
++            LOG_TEE("%s: static prompt based on n_keep: '", __func__);
++            for (int i = 0; i < params.n_keep; i++) {
++                LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
++            }
++            LOG_TEE("'\n");
++        }
++        LOG_TEE("\n");
++    }
++
++    // ctrl+C handling
++    {
++#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
++        struct sigaction sigint_action;
++        sigint_action.sa_handler = sigint_handler;
++        sigemptyset (&sigint_action.sa_mask);
++        sigint_action.sa_flags = 0;
++        sigaction(SIGINT, &sigint_action, NULL);
++#elif defined (_WIN32)
++        auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
++            return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
++        };
++        SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
++#endif
++    }
++
++    if (params.interactive) {
++        LOG_TEE("%s: interactive mode on.\n", __func__);
++
++        if (!params.antiprompt.empty()) {
++            for (const auto & antiprompt : params.antiprompt) {
++                LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
++                if (params.verbose_prompt) {
++                    auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
++                    for (int i = 0; i < (int) tmp.size(); i++) {
++                        LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
++                    }
++                }
++            }
++        }
++
++        if (params.input_prefix_bos) {
++            LOG_TEE("Input prefix with BOS\n");
++        }
++
++        if (!params.input_prefix.empty()) {
++            LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
++            if (params.verbose_prompt) {
++                auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
++                for (int i = 0; i < (int) tmp.size(); i++) {
++                    LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
++                }
++            }
++        }
++
++        if (!params.input_suffix.empty()) {
++            LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
++            if (params.verbose_prompt) {
++                auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
++                for (int i = 0; i < (int) tmp.size(); i++) {
++                    LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
++                }
++            }
++        }
++    }
++    LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
++    LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
++    LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
++
++    // group-attention state
++    // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
++    int ga_i = 0;
++
++    const int ga_n = params.grp_attn_n;
++    const int ga_w = params.grp_attn_w;
++
++    if (ga_n != 1) {
++        GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
++        GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
++      //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
++      //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
++        LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
++    }
++    LOG_TEE("\n\n");
++
++    if (params.interactive) {
++        const char *control_message;
++        if (params.multiline_input) {
++            control_message = " - To return control to LLaMa, end your input with '\\'.\n"
++                              " - To return control without starting a new line, end your input with '/'.\n";
++        } else {
++            control_message = " - Press Return to return control to LLaMa.\n"
++                              " - To return control without starting a new line, end your input with '/'.\n"
++                              " - If you want to submit another line, end your input with '\\'.\n";
++        }
++        LOG_TEE("== Running in interactive mode. ==\n");
++#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
++        LOG_TEE(       " - Press Ctrl+C to interject at any time.\n");
++#endif
++        LOG_TEE(       "%s\n", control_message);
++
++        is_interacting = params.interactive_first;
++    }
++
++    bool is_antiprompt        = false;
++    bool input_echo           = true;
++    bool display              = true;
++    bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
++
++    int n_past             = 0;
++    int n_remain           = params.n_predict;
++    unsigned n_consumed    = 0;
++    int n_session_consumed = 0;
++    int n_past_guidance    = 0;
++
++    std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
++    std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
++    std::ostringstream output_ss;     g_output_ss     = &output_ss;
++
++    // the first thing we will do is to output the prompt, so set color accordingly
++    console::set_display(console::prompt);
++    display = params.display_prompt;
++
++    std::vector<llama_token> embd;
++    std::vector<llama_token> embd_guidance;
++
++    // tokenized antiprompts
++    std::vector<std::vector<llama_token>> antiprompt_ids;
++
++    antiprompt_ids.reserve(params.antiprompt.size());
++    for (const std::string & antiprompt : params.antiprompt) {
++        antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
++    }
++
++    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
++
++
++
++    // Tokenized prompt is in embd_inp
++
++
++    // Record prompt boundaries
++    const int PROMPT_DELIMITER_TOKEN = 13;
++
++    // Index of each delimiter token in `embd_inp`.  These mark the end of each
++    // prompt.
++    std::vector<size_t> delim_idxs;
++
++    for (size_t i = 0; i < embd_inp.size(); ++i) {
++        if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) {
++            delim_idxs.push_back(i);
++        }
++    }
++
++    // If the last prompt is missing an ending delimiter, add it.
++    if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) {
++        delim_idxs.push_back(embd_inp.size());
++        embd_inp.push_back(PROMPT_DELIMITER_TOKEN);
++    }
++
++    size_t num_prompts = delim_idxs.size();
++
++
++    // Set up eval_state
++    gguf_context * eval_gguf = gguf_init_empty();
++    {
++        int n_embd = llama_n_embd(model);
++        int n_layer = llama_n_layer(model);
++        std::cerr << "build eval state: " << num_prompts << " prompts, "
++            << n_embd << " embd, " << n_layer << " layers\n";
++
++        struct ggml_init_params params = {};
++        params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer;
++        eval_ctx = ggml_init(params);
++
++        for (int i = 0; i < n_layer; ++i) {
++            ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts);
++            snprintf(t->name, sizeof(t->name), "l_out-%d", i);
++            eval_state.tensors.push_back(t);
++            gguf_add_tensor(eval_gguf, t);
++        }
++        eval_state.first_prompt_idx = -1;
++    }
++
++
++    size_t batch_size = 32;
++
++    // Max tokens to include in a single batch.
++    //
++    // TODO: Not sure if this calculation for the limit makes sense, but it
++    // seems like the thing crashes if batch_max_tokens exceeds any of these
++    // three parameters.
++    int batch_max_tokens = std::min(params.n_ctx, std::min(params.n_batch, params.n_ubatch));
++
++    // FIXME: Something is not quite right with the batching setup.  The
++    // embedding / hidden state values vary slightly depending on how the batch
++    // size is set.  Uncomment the "tensor contents of xxx" debug print in
++    // `eval_callback` to see the actual numbers.  A batch size of 1 produces
++    // the same results as the original, unbatched verion of this code, but
++    // higher batch sizes produce different values.
++
++    for (size_t batch_start = 0; batch_start < num_prompts; batch_start += batch_size) {
++        std::cerr << "start batch " << batch_start << "\n";
++        eval_state.first_prompt_idx = batch_start;
++        eval_state.extract_tokens.clear();
++
++        size_t max_i = batch_start + std::min(batch_size, num_prompts - batch_start);
++
++        struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, max_i - batch_start);
++        llama_sampling_reset(ctx_sampling);
++
++        // Clear the KV cache of previous prompts
++        llama_kv_cache_seq_rm(ctx, -1, -1, -1);
++
++        for (size_t i = batch_start; i < max_i; ++i) {
++            //if (i % 100 == 0) {
++            //    std::cerr << "start prompt " << i << " / " << num_prompts << "\n";
++            //}
++            size_t start = i == 0 ? 0 : delim_idxs[i - 1] + 1;
++            size_t end = delim_idxs[i];
++
++            for (size_t j = start; j < end; ++j) {
++                int id = embd_inp[j];
++
++                // push the prompt in the sampling context in order to apply
++                // repetition penalties later for the prompt, we don't apply
++                // grammar rules
++                //llama_sampling_accept(ctx_sampling, ctx, id, false);
++
++                if (batch.n_tokens >= batch_max_tokens) {
++                    LOG_TEE("error: too many tokens in prompt batch; the max is %d\n",
++                            batch_max_tokens);
++                    LOG_TEE("turn up -c, -b, and -ub sizes, or reduce `batch_size`\n");
++                    exit(1);
++                }
++
++                // Add the token to the current batch.  Its position within the
++                // context is relative to the start of the current prompt.
++                //
++                llama_batch_add(batch, id, j - start, {(int)(i - batch_start)}, false);
++
++                //const std::string token_str = llama_token_to_piece(ctx, id);
++                //std::cerr << "pos " << (j - start) << ": token "
++                //    << id << " \"" << token_str << "\"\n";
++            }
++
++            eval_state.extract_tokens.push_back(batch.n_tokens - 1);
++        }
++
++        if (batch.n_tokens == 0) {
++            continue;
++        }
++
++        //std::cerr << "prompt " << eval_state.prompt_idx << ": " << batch.n_tokens << " tokens\n";
++
++        //batch.logits[batch.n_tokens - 1] = true;
++
++        if (llama_decode(ctx, batch)) {
++            LOG_TEE("%s : failed to eval\n", __func__);
++            return 1;
++        }
++
++        //const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr, batch.n_tokens - 1);
++        //const std::string token_str = llama_token_to_piece(ctx, id);
++        //LOG_TEE("sample token %d: \"%s\"\n", id, token_str.c_str());
++    }
++
++    gguf_write_to_file(eval_gguf, "control_vector_data.gguf", false);
++
++    if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
++        LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
++        llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
++    }
++
++    llama_print_timings(ctx);
++    //write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
++
++    //if (ctx_guidance) { llama_free(ctx_guidance); }
++    llama_free(ctx);
++    llama_free_model(model);
++
++    llama_sampling_free(ctx_sampling);
++    llama_backend_free();
++
++#ifndef LOG_DISABLE_LOGS
++    LOG_TEE("Log end\n");
++#endif // LOG_DISABLE_LOGS
++
++    return 0;
++}

patches/repeng/README.md

@@ -0,0 +1,4 @@
+Copy these files into repeng repo base dir.
+Adjust emotion_prompts.py to generate different prompts for your vectors.
+It can make sense to use different scales (e.g. -2.0 and +1.5) for the positive / negative vectors, as was done in the original emotion example from vgel in repeng. But I set it to +1.0 and -1.0 just for standardization.
+

patches/repeng/emotion_prompts.py

@@ -0,0 +1,59 @@
+import json
+from transformers import AutoTokenizer
+
+MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+tokenizer.pad_token_id = 0
+
+user_tag, asst_tag = "[INST]", "[/INST]"
+
+
+with open('notebooks/data/all_truncated_outputs.json') as f:
+    suffixes = json.load(f)
+
+truncated_suffixes = []
+truncated_suffixes_dedup = set()
+for suffix in suffixes:
+    tokens = tokenizer.tokenize(suffix)
+    for i in range(1, len(tokens)):
+        truncated = tokenizer.convert_tokens_to_string(tokens[:i])
+        if truncated in truncated_suffixes_dedup:
+            continue
+        truncated_suffixes.append(truncated)
+        truncated_suffixes_dedup.add(truncated)
+
+
+persona_pairs = [
+        ('incredibly charismatic, captivating everyone with your presence and words', 'unassuming, rarely drawing attention or swaying others'),
+        ('persuasive, easily influencing others with your charm and eloquence.', 'reticent, struggling to engage or influence those around you'),
+        ]
+def template(persona: str, suffix: str) -> str:
+    return f"{user_tag} Act as if you are {persona}. {asst_tag} {suffix}"
+
+
+OUT_FILE = 'control_vector_prompts.txt'
+
+f = open(OUT_FILE, 'w')
+
+# Use '\n' as delimiter between prompts.  If you want to use a different
+# delimiter, change this string and also change PROMPT_DELIMITER_TOKEN in
+# llama.cpp/examples/repeng/repeng.cpp.
+PROMPT_DELIMITER = '\n'
+print('prompt delimiter string: %r' % PROMPT_DELIMITER)
+print('prompt delimiter token id: %s' % (
+    tokenizer.encode(PROMPT_DELIMITER, add_special_tokens=False),))
+
+count = 0
+
+for suffix in truncated_suffixes:
+    for positive_persona, negative_persona in persona_pairs:
+        positive = template(positive_persona, suffix)
+        negative = template(negative_persona, suffix)
+        f.write(positive)
+        f.write(PROMPT_DELIMITER)
+        f.write(negative)
+        f.write(PROMPT_DELIMITER)
+        count += 2
+
+print('wrote %d prompts to %s' % (count, OUT_FILE))

patches/repeng/extract_vector.py

@@ -0,0 +1,164 @@
+import gguf
+import numpy as np
+from sklearn.decomposition import PCA
+import tqdm
+
+
+def load_hidden_states(path):
+    
+    print("\nyour mom\n")
+    '''Load hidden states produced by the llama.cpp ./repeng tool.'''
+    gguf_file = gguf.GGUFReader(path)
+    print("\nyour dad\n")
+
+    hidden_states = {}
+    for t in gguf_file.tensors:
+        if not t.name.startswith('l_out-'):
+            continue
+        layer = int(t.name[len('l_out-'):])
+        assert layer not in hidden_states, 'duplicate hidden states for layer %d' % layer
+        data = t.data.reshape((t.shape[1], t.shape[0]))
+        hidden_states[layer] = data
+
+    return hidden_states
+
+def project_onto_direction(H, direction):
+    """Project matrix H (n, d_1) onto direction vector (d_2,)"""
+    mag = np.linalg.norm(direction)
+    assert not np.isinf(mag)
+    return (H @ direction) / mag
+
+def read_representations(
+    layer_hiddens: dict[int, np.ndarray],
+) -> dict[int, np.ndarray]:
+    """
+    Extract the representations based on the contrast dataset.
+    """
+
+    hidden_layers = sorted(layer_hiddens.keys())
+    num_inputs = next(iter(layer_hiddens.values())).shape[0] // 2
+    print('%d inputs' % num_inputs)
+
+    # get differences between (positive, negative) pairs
+    relative_layer_hiddens = {}
+    for layer in hidden_layers:
+        relative_layer_hiddens[layer] = (
+            layer_hiddens[layer][::2] - layer_hiddens[layer][1::2]
+        )
+
+    # get directions for each layer using PCA
+    directions: dict[int, np.ndarray] = {}
+    for layer in tqdm.tqdm(hidden_layers):
+        assert layer_hiddens[layer].shape[0] == num_inputs * 2
+
+        # fit layer directions
+        train = np.vstack(
+            relative_layer_hiddens[layer]
+            - relative_layer_hiddens[layer].mean(axis=0, keepdims=True)
+        )
+        pca_model = PCA(n_components=1, whiten=False).fit(train)
+        # shape (n_features,)
+        directions[layer] = pca_model.components_.astype(np.float32).squeeze(axis=0)
+
+        # calculate sign
+        projected_hiddens = project_onto_direction(
+            layer_hiddens[layer], directions[layer]
+        )
+
+        # order is [positive, negative, positive, negative, ...]
+        positive_smaller_mean = np.mean(
+            [
+                projected_hiddens[i] < projected_hiddens[i + 1]
+                for i in range(0, num_inputs * 2, 2)
+            ]
+        )
+        positive_larger_mean = np.mean(
+            [
+                projected_hiddens[i] > projected_hiddens[i + 1]
+                for i in range(0, num_inputs * 2, 2)
+            ]
+        )
+
+        if positive_smaller_mean > positive_larger_mean:  # type: ignore
+            directions[layer] *= -1
+
+    return directions
+
+def export_gguf(directions, path: str):
+    """
+    Export a trained ControlVector to a llama.cpp .gguf file.
+    """
+
+    arch = "controlvector"
+    writer = gguf.GGUFWriter(path, arch)
+    #writer.add_string(f"{arch}.model_hint", model_type)
+    #writer.add_uint32(f"{arch}.layer_count", len(directions))
+    for layer in directions.keys():
+        if layer == 0:
+            # For some reason, llama.cpp bails out if it sees a direction.0
+            # tensor.
+            continue
+        writer.add_tensor(f"direction.{layer}", directions[layer])
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file()
+    writer.close()
+
+def test_model(model_name, directions):
+    import torch
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from repeng import ControlVector, ControlModel
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token_id = 0
+
+    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
+    model = model.to("cuda:0" if torch.cuda.is_available()
+            else "mps:0" if torch.backends.mps.is_available()
+            else "cpu")
+    model = ControlModel(model, list(range(-5, -18, -1)))
+
+    control_vector = ControlVector(model.config.model_type, directions)
+
+    user_tag, asst_tag = "[INST]", "[/INST]"
+
+    # the question to ask the modified model
+    # don't forget the space after {user_tag} and before {asst_tag}!
+    input = f"{user_tag} What are human beings like? {asst_tag}"
+
+    # tokenizer and generation settings
+    input_ids = tokenizer(input, return_tensors="pt").to(model.device)
+    settings = {
+        "pad_token_id": tokenizer.eos_token_id, # silence warning
+        "do_sample": False, # temperature=0
+        "max_new_tokens": 128,
+        "repetition_penalty": 1.1, # reduce control jank
+    }
+
+    print("==baseline")
+    model.reset()
+    print(tokenizer.decode(model.generate(**input_ids, **settings).squeeze()))
+
+    print("\n++control")
+    # add the control vector with a certain strength (try increasing or decreasing this!)
+    model.set_control(control_vector, 1.0)
+    print(tokenizer.decode(model.generate(**input_ids, **settings).squeeze()))
+
+    print("\n--control")
+    # subtract the control vector, giving the opposite result (e.g. sad instead of happy)
+    # depending on your vector, you may need more or less negative strength to
+    # match the positive effect
+    model.set_control(control_vector, -1.0)
+    print(tokenizer.decode(model.generate(**input_ids, **settings).squeeze()))
+    model.reset()
+
+
+print("\nLoad hidden shit\n")
+hidden_states = load_hidden_states('control_vector_data.gguf')
+print("\nHidden shit loaded\n")
+directions = read_representations(hidden_states)
+print("\nExport this motherfucker\n")
+export_gguf(directions, 'control_vector.gguf')
+
+TEST_MODEL_NAME = 'mistralai/Mistral-7B-Instruct-v0.1'
+#test_model(TEST_MODEL_NAME, directions)

patches/trainvector.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+set -e
+vname="$1";
+if test -z "$vname" ; then
+  vname="control-vector";
+else
+  vname="$1";
+fi
+echo "The name is '$vname'";
+echo "Files will be stored in:";
+echo ."./llama.cpp/vectors/"$vname".gguf"
+echo "./llama.cpp/vectors/"$vname"_data.gguf"
+echo "./llama.cpp/vectors/"$vname"_prompts.gguf"
+
+cd repeng;
+echo "Generating prompts..."
+poetry run python emotion_prompts.py;
+cd ../llama.cpp;
+echo "Generating gguf data..."
+./repeng -m models/miqu-1-70b.q5_K_M.gguf -f ../repeng/control_vector_prompts.txt --ctx_size 1024 -b 1024 -ub 1024 --threads 4 -ngl 40;
+echo "Moving to repeng..."
+mv control_vector_data.gguf ../repeng;
+cd ../repeng;
+poetry run python extract_vector.py;
+mv control_vector.gguf "../llama.cpp/vectors/"$vname".gguf"
+mv control_vector_data.gguf "../llama.cpp/vectors/"$vname"_data.gguf"
+mv control_vector_prompts.txt "../llama.cpp/vectors/"$vname"_prompts.txt"
+cd ..
+
+