#include "llama-model.h"

#include "llama-impl.h"

#include <algorithm>
#include <cassert>
#include <stdexcept>

const char * llm_type_name(llm_type type) {
    switch (type) {
        case MODEL_14M:           return "14M";
        case MODEL_17M:           return "17M";
        case MODEL_22M:           return "22M";
        case MODEL_33M:           return "33M";
        case MODEL_60M:           return "60M";
        case MODEL_70M:           return "70M";
        case MODEL_80M:           return "80M";
        case MODEL_109M:          return "109M";
        case MODEL_137M:          return "137M";
        case MODEL_160M:          return "160M";
        case MODEL_220M:          return "220M";
        case MODEL_250M:          return "250M";
        case MODEL_270M:          return "270M";
        case MODEL_335M:          return "335M";
        case MODEL_410M:          return "410M";
        case MODEL_450M:          return "450M";
        case MODEL_770M:          return "770M";
        case MODEL_780M:          return "780M";
        case MODEL_0_5B:          return "0.5B";
        case MODEL_1B:            return "1B";
        case MODEL_1_3B:          return "1.3B";
        case MODEL_1_4B:          return "1.4B";
        case MODEL_1_5B:          return "1.5B";
        case MODEL_1_6B:          return "1.6B";
        case MODEL_2B:            return "2B";
        case MODEL_2_8B:          return "2.8B";
        case MODEL_3B:            return "3B";
        case MODEL_4B:            return "4B";
        case MODEL_6B:            return "6B";
        case MODEL_6_9B:          return "6.9B";
        case MODEL_7B:            return "7B";
        case MODEL_8B:            return "8B";
        case MODEL_9B:            return "9B";
        case MODEL_11B:           return "11B";
        case MODEL_12B:           return "12B";
        case MODEL_13B:           return "13B";
        case MODEL_14B:           return "14B";
        case MODEL_15B:           return "15B";
        case MODEL_16B:           return "16B";
        case MODEL_20B:           return "20B";
        case MODEL_30B:           return "30B";
        case MODEL_32B:           return "32B";
        case MODEL_34B:           return "34B";
        case MODEL_35B:           return "35B";
        case MODEL_40B:           return "40B";
        case MODEL_65B:           return "65B";
        case MODEL_70B:           return "70B";
        case MODEL_236B:          return "236B";
        case MODEL_314B:          return "314B";
        case MODEL_SMALL:         return "0.1B";
        case MODEL_MEDIUM:        return "0.4B";
        case MODEL_LARGE:         return "0.8B";
        case MODEL_XL:            return "1.5B";
        case MODEL_A1_7B:         return "A1.7B";
        case MODEL_A2_7B:         return "A2.7B";
        case MODEL_8x7B:          return "8x7B";
        case MODEL_8x22B:         return "8x22B";
        case MODEL_16x12B:        return "16x12B";
        case MODEL_10B_128x3_66B: return "10B+128x3.66B";
        case MODEL_57B_A14B:      return "57B.A14B";
        case MODEL_27B:           return "27B";
        default:                  return "?B";
    }
}

static std::string llama_model_ftype_name(llama_ftype ftype) {
    if (ftype & LLAMA_FTYPE_GUESSED) {
        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
    }

    switch (ftype) {
        case LLAMA_FTYPE_ALL_F32:         return "all F32";
        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
        case LLAMA_FTYPE_MOSTLY_TQ1_0:    return "TQ1_0 - 1.69 bpw ternary";
        case LLAMA_FTYPE_MOSTLY_TQ2_0:    return "TQ2_0 - 2.06 bpw ternary";
        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";

        default: return "unknown, may not work";
    }
}

std::string llama_model_arch_name (const llama_model & model) {
    return llm_arch_name(model.arch);
}

std::string llama_model_type_name (const llama_model & model) {
    return llm_type_name(model.type);
}

std::string llama_model_ftype_name(const llama_model & model) {
    return llama_model_ftype_name(model.ftype);
}

template<typename F>
static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
    ggml_init_params params = {
        /*.mem_size   =*/ ggml_tensor_overhead()*8,
        /*.mem_buffer =*/ NULL,
        /*.no_alloc   =*/ true,
    };

    ggml_context_ptr ctx { ggml_init(params) };
    if (!ctx) {
        throw std::runtime_error(format("failed to create ggml context"));
    }

    ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
    ggml_tensor * op_tensor = fn(ctx.get());
    for (int i = 0; i < GGML_MAX_SRC; i++) {
        if (op_tensor->src[i] != nullptr) {
            assert(op_tensor->src[i]->buffer == nullptr);
            op_tensor->src[i]->buffer = buf.get();
        }
    }

    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);

    return op_supported;
}

template<typename F>
static ggml_backend_buffer_type_t select_buft(const llama_model::buft_list_t & buft_list, const F & fn) {
    for (const auto & cur : buft_list) {
        ggml_backend_dev_t cur_dev = cur.first;
        ggml_backend_buffer_type_t cur_buft = cur.second;
        if (buft_supported(cur_buft, cur_dev, fn)) {
            return cur_buft;
        }
    }

    throw std::runtime_error(format("no suitable buffer type found"));
}

ggml_backend_buffer_type_t llama_model_select_buft(const llama_model & model, int il) {
    return select_buft(
            *model.dev_layer.at(il).buft_list,
            [&](ggml_context * ctx) {
                ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
                ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
                return ggml_add(ctx, cur, layer_dir);
            });
}

struct ggml_tensor * llama_model_get_tensor(const struct llama_model & model, const char * name) {
    auto it = std::find_if(model.tensors_by_name.begin(), model.tensors_by_name.end(),
            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
                return it.first == name;
            });
    if (it == model.tensors_by_name.end()) {
        return nullptr;
    }

    return it->second;
}

size_t llama_model_max_nodes(const llama_model & model) {
    return std::max<size_t>(8192, model.tensors_by_name.size()*5);
}

//
// interface implementation
//

struct llama_model_params llama_model_default_params() {
    struct llama_model_params result = {
        /*.devices                     =*/ nullptr,
        /*.n_gpu_layers                =*/ 0,
        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
        /*.main_gpu                    =*/ 0,
        /*.tensor_split                =*/ nullptr,
        /*.rpc_servers                 =*/ nullptr,
        /*.progress_callback           =*/ nullptr,
        /*.progress_callback_user_data =*/ nullptr,
        /*.kv_overrides                =*/ nullptr,
        /*.vocab_only                  =*/ false,
        /*.use_mmap                    =*/ true,
        /*.use_mlock                   =*/ false,
        /*.check_tensors               =*/ false,
    };

#ifdef GGML_USE_METAL
    // note: we usually have plenty of VRAM, so by default offload all layers to the GPU
    result.n_gpu_layers = 999;
#endif

    return result;
}

void llama_free_model(struct llama_model * model) {
    delete model;
}

enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
    return model->vocab.type;
}

int32_t llama_n_vocab(const struct llama_model * model) {
    return model->hparams.n_vocab;
}

int32_t llama_n_ctx_train(const struct llama_model * model) {
    return model->hparams.n_ctx_train;
}

int32_t llama_n_embd(const struct llama_model * model) {
    return model->hparams.n_embd;
}

int32_t llama_n_layer(const struct llama_model * model) {
    return model->hparams.n_layer;
}

int32_t llama_n_head(const struct llama_model * model) {
    return model->hparams.n_head();
}

enum llama_rope_type llama_rope_type(const struct llama_model * model) {
    switch (model->arch) {
        // these models do not use RoPE
        case LLM_ARCH_GPT2:
        case LLM_ARCH_GPTJ:
        case LLM_ARCH_MPT:
        case LLM_ARCH_REFACT:
        case LLM_ARCH_BLOOM:
        case LLM_ARCH_MAMBA:
        case LLM_ARCH_JINA_BERT_V2:
        case LLM_ARCH_T5:
        case LLM_ARCH_T5ENCODER:
        case LLM_ARCH_JAIS:
        case LLM_ARCH_RWKV6:
        case LLM_ARCH_WAVTOKENIZER_DEC:
            return LLAMA_ROPE_TYPE_NONE;

        // use what we call a normal RoPE, operating on pairs of consecutive head values
        case LLM_ARCH_LLAMA:
        case LLM_ARCH_DECI:
        case LLM_ARCH_BAICHUAN:
        case LLM_ARCH_STARCODER:
        case LLM_ARCH_PLAMO:
        case LLM_ARCH_ORION:
        case LLM_ARCH_INTERNLM2:
        case LLM_ARCH_MINICPM:
        case LLM_ARCH_XVERSE:
        case LLM_ARCH_COMMAND_R:
        case LLM_ARCH_OLMO:
        case LLM_ARCH_ARCTIC:
        case LLM_ARCH_DEEPSEEK:
        case LLM_ARCH_DEEPSEEK2:
        case LLM_ARCH_CHATGLM:
        case LLM_ARCH_GRANITE:
        case LLM_ARCH_GRANITE_MOE:
        case LLM_ARCH_CHAMELEON:
            return LLAMA_ROPE_TYPE_NORM;

        // the pairs of head values are offset by n_rot/2
        case LLM_ARCH_FALCON:
        case LLM_ARCH_GROK:
        case LLM_ARCH_DBRX:
        case LLM_ARCH_BERT:
        case LLM_ARCH_NOMIC_BERT:
        case LLM_ARCH_STABLELM:
        case LLM_ARCH_BITNET:
        case LLM_ARCH_QWEN:
        case LLM_ARCH_QWEN2:
        case LLM_ARCH_QWEN2MOE:
        case LLM_ARCH_OLMO2:
        case LLM_ARCH_OLMOE:
        case LLM_ARCH_PHI2:
        case LLM_ARCH_PHI3:
        case LLM_ARCH_GEMMA:
        case LLM_ARCH_GEMMA2:
        case LLM_ARCH_STARCODER2:
        case LLM_ARCH_OPENELM:
        case LLM_ARCH_GPTNEOX:
        case LLM_ARCH_CODESHELL:
        case LLM_ARCH_NEMOTRON:
        case LLM_ARCH_EXAONE:
        case LLM_ARCH_MINICPM3:
            return LLAMA_ROPE_TYPE_NEOX;

        case LLM_ARCH_QWEN2VL:
            return LLAMA_ROPE_TYPE_MROPE;

        // all model arches should be listed explicitly here
        case LLM_ARCH_UNKNOWN:
            GGML_ABORT("unknown architecture");
    }

    return LLAMA_ROPE_TYPE_NONE;
}

float llama_rope_freq_scale_train(const struct llama_model * model) {
    return model->hparams.rope_freq_scale_train;
}

int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
    const auto & it = model->gguf_kv.find(key);
    if (it == model->gguf_kv.end()) {
        if (buf_size > 0) {
            buf[0] = '\0';
        }
        return -1;
    }
    return snprintf(buf, buf_size, "%s", it->second.c_str());
}

int32_t llama_model_meta_count(const struct llama_model * model) {
    return (int)model->gguf_kv.size();
}

int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
    if (i < 0 || i >= (int)model->gguf_kv.size()) {
        if (buf_size > 0) {
            buf[0] = '\0';
        }
        return -1;
    }
    auto it = model->gguf_kv.begin();
    std::advance(it, i);
    return snprintf(buf, buf_size, "%s", it->first.c_str());
}

int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
    if (i < 0 || i >= (int)model->gguf_kv.size()) {
        if (buf_size > 0) {
            buf[0] = '\0';
        }
        return -1;
    }
    auto it = model->gguf_kv.begin();
    std::advance(it, i);
    return snprintf(buf, buf_size, "%s", it->second.c_str());
}

int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
    return snprintf(buf, buf_size, "%s %s %s",
            llama_model_arch_name (*model).c_str(),
            llama_model_type_name (*model).c_str(),
            llama_model_ftype_name(*model).c_str());
}

uint64_t llama_model_size(const struct llama_model * model) {
    return model->n_bytes;
}

uint64_t llama_model_n_params(const struct llama_model * model) {
    return model->n_elements;
}

bool llama_model_has_encoder(const struct llama_model * model) {
    switch (model->arch) {
        case LLM_ARCH_T5:        return true;
        case LLM_ARCH_T5ENCODER: return true;
        default:                 return false;
    }
}

bool llama_model_has_decoder(const struct llama_model * model) {
    switch (model->arch) {
        case LLM_ARCH_T5ENCODER: return false;
        default:                 return true;
    }
}

llama_token llama_model_decoder_start_token(const struct llama_model * model) {
    return model->hparams.dec_start_token_id;
}

bool llama_model_is_recurrent(const struct llama_model * model) {
    switch (model->arch) {
        case LLM_ARCH_MAMBA:  return true;
        case LLM_ARCH_RWKV6:  return true;
        default:              return false;
    }
}