Brunobkr/llama.cpp_AlgMor24_github

1#include "llama-adapter.h"2 3#include "llama-impl.h"4#include "llama-mmap.h"5#include "llama-model.h"6 7#include <map>8#include <cassert>9#include <sstream>10#include <stdexcept>11 12// vec13 14ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {15    if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {16        return nullptr;17    }18 19    return tensors[il];20}21 22ggml_tensor * llama_adapter_cvec::apply_to(ggml_context * ctx, ggml_tensor * cur, int  il) const {23    ggml_tensor * layer_dir = tensor_for(il);24    if (layer_dir != nullptr) {25        cur = ggml_add(ctx, cur, layer_dir);26    }27 28    return cur;29}30 31bool llama_adapter_cvec::init(const llama_model & model) {32    const auto & hparams = model.hparams;33 34    GGML_ASSERT(tensors.empty());35    GGML_ASSERT(ctxs.empty());36    GGML_ASSERT(bufs.empty());37 38    // create a context for each buffer type39    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;40    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {41        auto it = ctx_map.find(buft);42        if (it == ctx_map.end()) {43            ggml_init_params params = {44                /*.mem_size   =*/ hparams.n_layer()*ggml_tensor_overhead(),45                /*.mem_buffer =*/ NULL,46                /*.no_alloc   =*/ true,47            };48 49            ggml_context * ctx = ggml_init(params);50            if (!ctx) {51                return nullptr;52            }53 54            ctx_map[buft] = ctx;55            ctxs.emplace_back(ctx);56 57            return ctx;58        }59 60        return it->second;61    };62 63    // make tensors64    tensors.reserve(hparams.n_layer());65    tensors.push_back(nullptr); // there's never a tensor for layer 066    for (size_t il = 1; il < hparams.n_layer(); il++) {67        ggml_backend_buffer_type_t buft = model.select_buft(il);68        ggml_context * ctx = ctx_for_buft(buft);69        if (!ctx) {70            LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);71            return false;72        }73        ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);74        tensors.push_back(tensor);75    }76 77    // allocate tensors / buffers and zero78    bufs.reserve(ctx_map.size());79    for (auto it : ctx_map) {80        ggml_backend_buffer_type_t buft = it.first;81        ggml_context * ctx = it.second;82        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);83        if (!buf) {84            LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);85            return false;86        }87        ggml_backend_buffer_clear(buf, 0);88        bufs.emplace_back(buf);89    }90 91    return true;92}93 94bool llama_adapter_cvec::apply(95        const llama_model & model,96        const float * data,97        size_t len,98        int32_t n_embd,99        int32_t il_start,100        int32_t il_end) {101    const auto & hparams = model.hparams;102 103    if (data == nullptr) {104        // disable the current control vector (but leave allocated for later)105        layer_start = -1;106        layer_end   = -1;107        return true;108    }109 110    if (n_embd != (int) hparams.n_embd) {111        LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);112        return false;113    }114 115    if (tensors.empty()) {116        if (!init(model)) {117            return false;118        }119    }120 121    layer_start = il_start;122    layer_end   = il_end;123 124    for (size_t il = 1; il < hparams.n_layer(); il++) {125        assert(tensors[il] != nullptr);126 127        const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present128        if (off + n_embd <= len) {129            ggml_backend_tensor_set(tensors[il], data + off, 0, n_embd * ggml_element_size(tensors[il]));130        }131    }132 133    return true;134}135 136// lora137 138llama_adapter_lora_weight * llama_adapter_lora::get_weight(ggml_tensor * w) {139    const std::string name(w->name);140 141    const auto pos = ab_map.find(name);142    if (pos != ab_map.end()) {143        return &pos->second;144    }145 146    return nullptr;147}148 149static void llama_adapter_lora_init_impl(llama_model & model, const char * path_lora, llama_adapter_lora & adapter) {150    LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);151 152    ggml_context * ctx_init;153    gguf_init_params meta_gguf_params = {154        /* .no_alloc = */ true,155        /* .ctx      = */ &ctx_init,156    };157 158    gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };159    if (!ctx_gguf) {160        throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));161    }162 163    ggml_context_ptr ctx { ctx_init };164 165    // check metadata166    {167        const gguf_context * gguf_ctx = ctx_gguf.get();168 169        LLAMA_LOG_INFO("%s: Dumping metadata keys/values.\n", __func__);170 171        // get metadata as string172        for (int i = 0; i < gguf_get_n_kv(gguf_ctx); i++) {173            gguf_type type = gguf_get_kv_type(gguf_ctx, i);174            const std::string type_name =175                type == GGUF_TYPE_ARRAY176                ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(gguf_ctx, i)), gguf_get_arr_n(gguf_ctx, i))177                : gguf_type_name(type);178            const char * name = gguf_get_key(gguf_ctx, i);179            const std::string value = gguf_kv_to_str(gguf_ctx, i);180 181            if (type != GGUF_TYPE_ARRAY) {182                adapter.gguf_kv.emplace(name, value);183            }184 185            const size_t MAX_VALUE_LEN = 40;186            std::string print_value = value.size() > MAX_VALUE_LEN ? format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()) : value;187            replace_all(print_value, "\n", "\\n");188 189            LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), print_value.c_str());190        }191 192        auto get_kv_str = [&](const std::string & key) -> std::string {193            int id = gguf_find_key(gguf_ctx, key.c_str());194            return id < 0 ? "" : std::string(gguf_get_val_str(gguf_ctx, id));195        };196        auto get_kv_f32 = [&](const std::string & key) -> float {197            int id = gguf_find_key(gguf_ctx, key.c_str());198            return id < 0 ? 0.0f : gguf_get_val_f32(gguf_ctx, id);199        };200        LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);201 202        auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));203        if (general_type != "adapter") {204            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);205        }206 207        auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));208        auto general_arch = llm_arch_from_string(general_arch_str);209        if (general_arch != model.arch) {210            throw std::runtime_error("model arch and LoRA arch mismatch");211        }212 213        auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));214        if (adapter_type != "lora") {215            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);216        }217 218        adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));219 220        // parse alora invocation sequence vector221        const auto & key = llm_kv(LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS);222        const int kid = gguf_find_key(ctx_gguf.get(), key.c_str());223        if (kid >= 0) {224            if (gguf_get_kv_type(ctx_gguf.get(), kid) != GGUF_TYPE_ARRAY) {225                throw std::runtime_error("invalid gguf type for " + key);226            }227            const auto arr_type = gguf_get_arr_type(ctx_gguf.get(), kid);228            if (arr_type != GGUF_TYPE_UINT32) {229                throw std::runtime_error("invalid gguf element type for " + key);230            }231            const size_t seq_len = gguf_get_arr_n(ctx_gguf.get(), kid);232            const void * data = gguf_get_arr_data(ctx_gguf.get(), kid);233            adapter.alora_invocation_tokens.resize(seq_len);234            std::copy(235                (const llama_token *)data,236                (const llama_token *)data + seq_len,237                adapter.alora_invocation_tokens.begin());238        }239    }240 241    int n_tensors = gguf_get_n_tensors(ctx_gguf.get());242 243    // contexts for each buffer type244    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;245    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {246        auto it = ctx_map.find(buft);247        if (it == ctx_map.end()) {248            // add a new context249            ggml_init_params params = {250                /*.mem_size   =*/ n_tensors*ggml_tensor_overhead(),251                /*.mem_buffer =*/ NULL,252                /*.no_alloc   =*/ true,253            };254            ggml_context * buft_ctx = ggml_init(params);255            if (!buft_ctx) {256                return nullptr;257            }258            ctx_map[buft] = buft_ctx;259            adapter.ctxs.emplace_back(buft_ctx);260            return buft_ctx;261        };262        return it->second;263    };264 265    // bundle lora_a and lora_b into pairs266    std::map<std::string, llama_adapter_lora_weight> ab_map;267    auto str_endswith = [](const std::string & str, const std::string & suffix) {268        return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;269    };270 271    for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {272        std::string name(cur->name);273        if (str_endswith(name, ".lora_a")) {274            replace_all(name, ".lora_a", "");275            if (ab_map.find(name) == ab_map.end()) {276                ab_map[name] = llama_adapter_lora_weight(cur, nullptr);277            } else {278                ab_map[name].a = cur;279            }280        } else if (str_endswith(name, ".lora_b")) {281            replace_all(name, ".lora_b", "");282            if (ab_map.find(name) == ab_map.end()) {283                ab_map[name] = llama_adapter_lora_weight(nullptr, cur);284            } else {285                ab_map[name].b = cur;286            }287        } else if (str_endswith(name, "_norm.weight")) {288            // TODO: add support for norm vector289            // for now, we don't really care because most adapters still work fine without it290            continue;291        } else {292            throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");293        }294    }295 296    // get extra buffer types of the CPU297    // TODO: a more general solution for non-CPU extra buft should be implemented in the future298    //       ref: https://github.com/ggml-org/llama.cpp/pull/12593#pullrequestreview-2718659948299    std::vector<ggml_backend_buffer_type_t> buft_extra;300    {301        auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);302        if (!cpu_dev) {303            throw std::runtime_error(format("%s: no CPU backend found", __func__));304        }305        auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);306 307        auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)308            ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");309 310        if (ggml_backend_dev_get_extra_bufts_fn) {311            ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);312            while (extra_bufts && *extra_bufts) {313                buft_extra.emplace_back(*extra_bufts);314                ++extra_bufts;315            }316        }317    }318 319    // add tensors320    for (auto & it : ab_map) {321        const std::string & name = it.first;322        llama_adapter_lora_weight & w = it.second;323        bool is_token_embd = str_endswith(name, "token_embd.weight");324 325        if (!w.a || !w.b) {326            throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");327        }328 329        // device buft and device ctx330        const auto * model_tensor = model.get_tensor(name.c_str());331        if (!model_tensor) {332            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");333        }334 335        auto * buft = ggml_backend_buffer_get_type(model_tensor->buffer);336 337        // do not load loras to extra buffer types (i.e. bufts for repacking) -> use the CPU in that case338        for (auto & ex : buft_extra) {339            if (ex == buft) {340                LLAMA_LOG_WARN("%s: lora for '%s' cannot use buft '%s', fallback to CPU\n", __func__, model_tensor->name, ggml_backend_buft_name(buft));341 342                auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);343                if (!cpu_dev) {344                    throw std::runtime_error(format("%s: no CPU backend found", __func__));345                }346                buft = ggml_backend_dev_buffer_type(cpu_dev);347 348                break;349            }350        }351 352        LLAMA_LOG_DEBUG("%s: lora for '%s' -> '%s'\n", __func__, model_tensor->name, ggml_backend_buft_name(buft));353 354        ggml_context * dev_ctx = ctx_for_buft(buft);355        // validate tensor shape356        if (is_token_embd) {357            // expect B to be non-transposed, A and B are flipped; see llm_build_inp_embd()358            if (model_tensor->ne[0] != w.b->ne[1] || model_tensor->ne[1] != w.a->ne[1]) {359                throw std::runtime_error("tensor '" + name + "' has incorrect shape (hint: maybe wrong base model?)");360            }361        } else {362            if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {363                throw std::runtime_error("tensor '" + name + "' has incorrect shape (hint: maybe wrong base model?)");364            }365            if (w.a->ne[1] != w.b->ne[0]) {366                throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");367            }368        }369 370        // save tensor to adapter371        ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);372        ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);373        ggml_set_name(tensor_a, w.a->name);374        ggml_set_name(tensor_b, w.b->name);375        adapter.ab_map[name] = llama_adapter_lora_weight(tensor_a, tensor_b);376    }377 378    // allocate tensors / buffers and zero379    {380        adapter.ctxs.reserve(ctx_map.size());381        adapter.bufs.reserve(ctx_map.size());382        for (auto & it : ctx_map) {383            ggml_backend_buffer_type_t buft = it.first;384            ggml_context * ctx_dev = it.second;385            ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };386            if (!buf) {387                throw std::runtime_error("failed to allocate buffer for lora adapter\n");388            }389            LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);390            adapter.bufs.emplace_back(std::move(buf));391        }392    }393 394    // set tensor data395    {396        llama_file gguf_file(path_lora, "rb");397        std::vector<uint8_t> read_buf;398        auto set_tensor = [&](ggml_tensor * orig, ggml_tensor * dev) {399            size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));400            size_t size = ggml_nbytes(orig);401            read_buf.resize(size);402            gguf_file.seek(offs, SEEK_SET);403            gguf_file.read_raw(read_buf.data(), size);404            ggml_backend_tensor_set(dev, read_buf.data(), 0, size);405        };406        for (auto & it : adapter.ab_map) {407            auto orig = ab_map[it.first];408            auto dev  = it.second;409            set_tensor(orig.a, dev.a);410            set_tensor(orig.b, dev.b);411        }412    }413 414    // register adapter with model415    model.loras.insert(&adapter);416 417    LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);418}419 420llama_adapter_lora * llama_adapter_lora_init(llama_model * model, const char * path_lora) {421    llama_adapter_lora * adapter = new llama_adapter_lora(model);422 423    try {424        llama_adapter_lora_init_impl(*model, path_lora, *adapter);425        return adapter;426    } catch (const std::exception & err) {427        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());428 429        delete adapter;430    }431 432    return nullptr;433}434 435int32_t llama_adapter_meta_val_str(const llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size) {436    const auto & it = adapter->gguf_kv.find(key);437    if (it == adapter->gguf_kv.end()) {438        if (buf_size > 0) {439            buf[0] = '\0';440        }441        return -1;442    }443    return snprintf(buf, buf_size, "%s", it->second.c_str());444}445 446int32_t llama_adapter_meta_count(const llama_adapter_lora * adapter) {447    return (int)adapter->gguf_kv.size();448}449 450int32_t llama_adapter_meta_key_by_index(const llama_adapter_lora * adapter, int i, char * buf, size_t buf_size) {451    if (i < 0 || i >= (int)adapter->gguf_kv.size()) {452        if (buf_size > 0) {453            buf[0] = '\0';454        }455        return -1;456    }457    auto it = adapter->gguf_kv.begin();458    std::advance(it, i);459    return snprintf(buf, buf_size, "%s", it->first.c_str());460}461 462int32_t llama_adapter_meta_val_str_by_index(const llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size) {463    if (i < 0 || i >= (int)adapter->gguf_kv.size()) {464        if (buf_size > 0) {465            buf[0] = '\0';466        }467        return -1;468    }469    auto it = adapter->gguf_kv.begin();470    std::advance(it, i);471    return snprintf(buf, buf_size, "%s", it->second.c_str());472}473 474void llama_adapter_lora_free(llama_adapter_lora * adapter) {475    if (adapter == nullptr) {476        return;477    }478 479    if (adapter->model != nullptr) {480        adapter->model->loras.erase(adapter);481        adapter->model = nullptr;482    }483 484    delete adapter;485}486 487uint64_t llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter) {488    if (!adapter) {489        return 0;490    }491    return adapter->alora_invocation_tokens.size();492}493 494const llama_token * llama_adapter_get_alora_invocation_tokens(const llama_adapter_lora * adapter) {495    GGML_ASSERT(adapter);496    return adapter->alora_invocation_tokens.data();497}498