llama.cpp

#define LLAMA_API_INTERNAL
#include "llama.h"

#include "unicode.h"

#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"

#ifdef GGML_USE_CUBLAS
#  include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#  include "ggml-opencl.h"
#elif defined(GGML_USE_VULKAN)
#  include "ggml-vulkan.h"
#elif defined(GGML_USE_SYCL)
#  include "ggml-sycl.h"
#elif defined(GGML_USE_KOMPUTE)
#   include "ggml-kompute.h"
#endif

#ifdef GGML_USE_METAL
#  include "ggml-metal.h"
#endif
#ifdef GGML_USE_MPI
#  include "ggml-mpi.h"
#endif
#ifndef QK_K
#  ifdef GGML_QKK_64
#    define QK_K 64
#  else
#    define QK_K 256
#  endif
#endif

#ifdef __has_include
    #if __has_include(<unistd.h>)
        #include <unistd.h>
        #if defined(_POSIX_MAPPED_FILES)
            #include <sys/mman.h>
            #include <fcntl.h>
        #endif
        #if defined(_POSIX_MEMLOCK_RANGE)
            #include <sys/resource.h>
        #endif
    #endif
#endif

#if defined(_WIN32)
    #define WIN32_LEAN_AND_MEAN
    #ifndef NOMINMAX
        #define NOMINMAX
    #endif
    #include <windows.h>
    #include <io.h>
#endif

#include <algorithm>
#include <array>
#include <cassert>
#include <cfloat>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <cstdarg>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <cwctype>
#include <forward_list>
#include <fstream>
#include <functional>
#include <initializer_list>
#include <locale>
#include <map>
#include <memory>
#include <mutex>
#include <numeric>
#include <queue>
#include <random>
#include <regex>
#include <set>
#include <sstream>
#include <thread>
#include <type_traits>
#include <unordered_map>

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_ATTRIBUTE_FORMAT(...)
#endif

#define LLAMA_MAX_NODES   8192
#define LLAMA_MAX_EXPERTS 8


//
// logging
//

LLAMA_ATTRIBUTE_FORMAT(2, 3)
static void llama_log_internal        (ggml_log_level level, const char* format, ...);
static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);

#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)

//
// helpers
//

static size_t utf8_len(char src) {
    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
    return lookup[highbits];
}

static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
    std::string result;
    for (size_t pos = 0; ; pos += search.length()) {
        auto new_pos = s.find(search, pos);
        if (new_pos == std::string::npos) {
            result += s.substr(pos, s.size() - pos);
            break;
        }
        result += s.substr(pos, new_pos - pos) + replace;
        pos = new_pos;
    }
    s = std::move(result);
}

static bool is_float_close(float a, float b, float abs_tol) {
    // Check for non-negative tolerance
    if (abs_tol < 0.0) {
        throw std::invalid_argument("Tolerance must be non-negative");
    }

    // Exact equality check
    if (a == b) {
        return true;
    }

    // Check for infinities
    if (std::isinf(a) || std::isinf(b)) {
        return false;
    }

    // Regular comparison using the provided absolute tolerance
    return std::fabs(b - a) <= abs_tol;
}

static void zeros(std::ofstream & file, size_t n) {
    char zero = 0;
    for (size_t i = 0; i < n; ++i) {
        file.write(&zero, 1);
    }
}

LLAMA_ATTRIBUTE_FORMAT(1, 2)
static std::string format(const char * fmt, ...) {
    va_list ap;
    va_list ap2;
    va_start(ap, fmt);
    va_copy(ap2, ap);
    int size = vsnprintf(NULL, 0, fmt, ap);
    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
    std::vector<char> buf(size + 1);
    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
    GGML_ASSERT(size2 == size);
    va_end(ap2);
    va_end(ap);
    return std::string(buf.data(), size);
}

//
// gguf constants (sync with gguf.py)
//

enum llm_arch {
    LLM_ARCH_LLAMA,
    LLM_ARCH_FALCON,
    LLM_ARCH_BAICHUAN,
    LLM_ARCH_GPT2,
    LLM_ARCH_GPTJ,
    LLM_ARCH_GPTNEOX,
    LLM_ARCH_MPT,
    LLM_ARCH_STARCODER,
    LLM_ARCH_PERSIMMON,
    LLM_ARCH_REFACT,
    LLM_ARCH_BERT,
    LLM_ARCH_NOMIC_BERT,
    LLM_ARCH_BLOOM,
    LLM_ARCH_STABLELM,
    LLM_ARCH_QWEN,
    LLM_ARCH_QWEN2,
    LLM_ARCH_PHI2,
    LLM_ARCH_PLAMO,
    LLM_ARCH_CODESHELL,
    LLM_ARCH_ORION,
    LLM_ARCH_INTERNLM2,
    LLM_ARCH_MINICPM,
    LLM_ARCH_GEMMA,
    LLM_ARCH_STARCODER2,
    LLM_ARCH_MAMBA,
    LLM_ARCH_UNKNOWN,
};

static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_LLAMA,           "llama"      },
    { LLM_ARCH_FALCON,          "falcon"     },
    { LLM_ARCH_GPT2,            "gpt2"       },
    { LLM_ARCH_GPTJ,            "gptj"       },
    { LLM_ARCH_GPTNEOX,         "gptneox"    },
    { LLM_ARCH_MPT,             "mpt"        },
    { LLM_ARCH_BAICHUAN,        "baichuan"   },
    { LLM_ARCH_STARCODER,       "starcoder"  },
    { LLM_ARCH_PERSIMMON,       "persimmon"  },
    { LLM_ARCH_REFACT,          "refact"     },
    { LLM_ARCH_BERT,            "bert"       },
    { LLM_ARCH_NOMIC_BERT,      "nomic-bert" },
    { LLM_ARCH_BLOOM,           "bloom"      },
    { LLM_ARCH_STABLELM,        "stablelm"   },
    { LLM_ARCH_QWEN,            "qwen"       },
    { LLM_ARCH_QWEN2,           "qwen2"      },
    { LLM_ARCH_PHI2,            "phi2"       },
    { LLM_ARCH_PLAMO,           "plamo"      },
    { LLM_ARCH_CODESHELL,       "codeshell"  },
    { LLM_ARCH_ORION,           "orion"      },
    { LLM_ARCH_INTERNLM2,       "internlm2"  },
    { LLM_ARCH_MINICPM,         "minicpm"    },
    { LLM_ARCH_GEMMA,           "gemma"      },
    { LLM_ARCH_STARCODER2,      "starcoder2" },
    { LLM_ARCH_MAMBA,           "mamba"      },
    { LLM_ARCH_UNKNOWN,         "(unknown)"  },
};

enum llm_kv {
    LLM_KV_GENERAL_ARCHITECTURE,
    LLM_KV_GENERAL_QUANTIZATION_VERSION,
    LLM_KV_GENERAL_ALIGNMENT,
    LLM_KV_GENERAL_NAME,
    LLM_KV_GENERAL_AUTHOR,
    LLM_KV_GENERAL_URL,
    LLM_KV_GENERAL_DESCRIPTION,
    LLM_KV_GENERAL_LICENSE,
    LLM_KV_GENERAL_SOURCE_URL,
    LLM_KV_GENERAL_SOURCE_HF_REPO,

    LLM_KV_CONTEXT_LENGTH,
    LLM_KV_EMBEDDING_LENGTH,
    LLM_KV_BLOCK_COUNT,
    LLM_KV_FEED_FORWARD_LENGTH,
    LLM_KV_USE_PARALLEL_RESIDUAL,
    LLM_KV_TENSOR_DATA_LAYOUT,
    LLM_KV_EXPERT_COUNT,
    LLM_KV_EXPERT_USED_COUNT,
    LLM_KV_POOLING_TYPE,

    LLM_KV_ATTENTION_HEAD_COUNT,
    LLM_KV_ATTENTION_HEAD_COUNT_KV,
    LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
    LLM_KV_ATTENTION_CLAMP_KQV,
    LLM_KV_ATTENTION_KEY_LENGTH,
    LLM_KV_ATTENTION_VALUE_LENGTH,
    LLM_KV_ATTENTION_LAYERNORM_EPS,
    LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
    LLM_KV_ATTENTION_CAUSAL,

    LLM_KV_ROPE_DIMENSION_COUNT,
    LLM_KV_ROPE_FREQ_BASE,
    LLM_KV_ROPE_SCALE_LINEAR,
    LLM_KV_ROPE_SCALING_TYPE,
    LLM_KV_ROPE_SCALING_FACTOR,
    LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
    LLM_KV_ROPE_SCALING_FINETUNED,

    LLM_KV_SSM_INNER_SIZE,
    LLM_KV_SSM_CONV_KERNEL,
    LLM_KV_SSM_STATE_SIZE,
    LLM_KV_SSM_TIME_STEP_RANK,

    LLM_KV_TOKENIZER_MODEL,
    LLM_KV_TOKENIZER_LIST,
    LLM_KV_TOKENIZER_TOKEN_TYPE,
    LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
    LLM_KV_TOKENIZER_SCORES,
    LLM_KV_TOKENIZER_MERGES,
    LLM_KV_TOKENIZER_BOS_ID,
    LLM_KV_TOKENIZER_EOS_ID,
    LLM_KV_TOKENIZER_UNK_ID,
    LLM_KV_TOKENIZER_SEP_ID,
    LLM_KV_TOKENIZER_PAD_ID,
    LLM_KV_TOKENIZER_ADD_BOS,
    LLM_KV_TOKENIZER_ADD_EOS,
    LLM_KV_TOKENIZER_ADD_PREFIX,
    LLM_KV_TOKENIZER_HF_JSON,
    LLM_KV_TOKENIZER_RWKV,
};

static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
    { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
    { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
    { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
    { LLM_KV_GENERAL_NAME,                  "general.name"                          },
    { LLM_KV_GENERAL_AUTHOR,                "general.author"                        },
    { LLM_KV_GENERAL_URL,                   "general.url"                           },
    { LLM_KV_GENERAL_DESCRIPTION,           "general.description"                   },
    { LLM_KV_GENERAL_LICENSE,               "general.license"                       },
    { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
    { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },

    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"   },
    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual" },
    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"    },
    { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"          },
    { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"     },
    { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"          },

    { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
    { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,      "%s.attention.max_alibi_bias"         },
    { LLM_KV_ATTENTION_CLAMP_KQV,           "%s.attention.clamp_kqv"              },
    { LLM_KV_ATTENTION_KEY_LENGTH,          "%s.attention.key_length"             },
    { LLM_KV_ATTENTION_VALUE_LENGTH,        "%s.attention.value_length"           },
    { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
    { LLM_KV_ATTENTION_CAUSAL,              "%s.attention.causal"                 },

    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },

    { LLM_KV_SSM_CONV_KERNEL,               "%s.ssm.conv_kernel"    },
    { LLM_KV_SSM_INNER_SIZE,                "%s.ssm.inner_size"     },
    { LLM_KV_SSM_STATE_SIZE,                "%s.ssm.state_size"     },
    { LLM_KV_SSM_TIME_STEP_RANK,            "%s.ssm.time_step_rank" },

    { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
    { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
    { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,    "tokenizer.ggml.token_type_count"   },
    { LLM_KV_TOKENIZER_SCORES,              "tokenizer.ggml.scores"             },
    { LLM_KV_TOKENIZER_MERGES,              "tokenizer.ggml.merges"             },
    { LLM_KV_TOKENIZER_BOS_ID,              "tokenizer.ggml.bos_token_id"       },
    { LLM_KV_TOKENIZER_EOS_ID,              "tokenizer.ggml.eos_token_id"       },
    { LLM_KV_TOKENIZER_UNK_ID,              "tokenizer.ggml.unknown_token_id"   },
    { LLM_KV_TOKENIZER_SEP_ID,              "tokenizer.ggml.seperator_token_id" },
    { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
    { LLM_KV_TOKENIZER_ADD_BOS,             "tokenizer.ggml.add_bos_token"      },
    { LLM_KV_TOKENIZER_ADD_EOS,             "tokenizer.ggml.add_eos_token"      },
    { LLM_KV_TOKENIZER_ADD_PREFIX,          "tokenizer.ggml.add_space_prefix"   },
    { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
    { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },
};

struct LLM_KV {
    LLM_KV(llm_arch arch) : arch(arch) {}

    llm_arch arch;

    std::string operator()(llm_kv kv) const {
        return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
    }
};

enum llm_tensor {
    LLM_TENSOR_TOKEN_EMBD,
    LLM_TENSOR_TOKEN_EMBD_NORM,
    LLM_TENSOR_TOKEN_TYPES,
    LLM_TENSOR_POS_EMBD,
    LLM_TENSOR_OUTPUT,
    LLM_TENSOR_OUTPUT_NORM,
    LLM_TENSOR_ROPE_FREQS,
    LLM_TENSOR_ATTN_Q,
    LLM_TENSOR_ATTN_K,
    LLM_TENSOR_ATTN_V,
    LLM_TENSOR_ATTN_QKV,
    LLM_TENSOR_ATTN_OUT,
    LLM_TENSOR_ATTN_NORM,
    LLM_TENSOR_ATTN_NORM_2,
    LLM_TENSOR_ATTN_OUT_NORM,
    LLM_TENSOR_ATTN_ROT_EMBD,
    LLM_TENSOR_FFN_GATE_INP,
    LLM_TENSOR_FFN_NORM,
    LLM_TENSOR_FFN_GATE,
    LLM_TENSOR_FFN_DOWN,
    LLM_TENSOR_FFN_UP,
    LLM_TENSOR_FFN_ACT,
    LLM_TENSOR_FFN_DOWN_EXP,
    LLM_TENSOR_FFN_GATE_EXP,
    LLM_TENSOR_FFN_UP_EXP,
    LLM_TENSOR_ATTN_Q_NORM,
    LLM_TENSOR_ATTN_K_NORM,
    LLM_TENSOR_LAYER_OUT_NORM,
    LLM_TENSOR_SSM_IN,
    LLM_TENSOR_SSM_CONV1D,
    LLM_TENSOR_SSM_X,
    LLM_TENSOR_SSM_DT,
    LLM_TENSOR_SSM_A,
    LLM_TENSOR_SSM_D,
    LLM_TENSOR_SSM_OUT,
};

static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
    {
        LLM_ARCH_LLAMA,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
        },
    },
    {
        LLM_ARCH_BAICHUAN,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_FALCON,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_GPT2,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_POS_EMBD,        "position_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
        },
    },
    {
        LLM_ARCH_GPTJ,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
        },
    },
    {
        LLM_ARCH_GPTNEOX,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_PERSIMMON,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd"},
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm"},
            { LLM_TENSOR_OUTPUT,          "output"},
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm"},
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv"},
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output"},
            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm"},
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down"},
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up"},
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd"},
        },
    },
    {
        LLM_ARCH_MPT,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_ACT,         "blk.%d.ffn.act" },
        },
    },
    {
        LLM_ARCH_STARCODER,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_POS_EMBD,        "position_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
        },
    },
    {
        LLM_ARCH_REFACT,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_BERT,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
            { LLM_TENSOR_POS_EMBD,        "position_embd" },
            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_NOMIC_BERT,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_BLOOM,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
        },
    },
    {
        LLM_ARCH_STABLELM,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_QWEN,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_QWEN2,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_PHI2,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_PLAMO,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_CODESHELL,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_ORION,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_INTERNLM2,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_MINICPM,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
        },
    },
    {
        LLM_ARCH_GEMMA,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_STARCODER2,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_MAMBA,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
        },
    },
    {
        LLM_ARCH_UNKNOWN,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
        },
    },
};

static llm_arch llm_arch_from_string(const std::string & name) {
    for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
        if (kv.second == name) {
            return kv.first;
        }
    }

    return LLM_ARCH_UNKNOWN;
}

// helper to handle gguf constants
// usage:
//
//   const auto tn = LLM_TN(LLM_ARCH_LLAMA);
//
//   std::string name = tn(LLM_TENSOR_OUTPUT);                     -> "output"
//   std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias");         -> "token_embd.bias"
//   std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3);     -> "blk.3.attn_norm.weight"
//
struct LLM_TN {
    LLM_TN(llm_arch arch) : arch(arch) {}

    llm_arch arch;

    std::string operator()(llm_tensor tensor) const {
        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
            return "__missing__";
        }
        return LLM_TENSOR_NAMES.at(arch).at(tensor);
    }

    std::string operator()(llm_tensor tensor, const std::string & suffix) const {
        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
            return "__missing__";
        }
        return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
    }

    std::string operator()(llm_tensor tensor, int bid) const {
        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
            return "__missing__";
        }
        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
    }

    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
            return "__missing__";
        }
        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
    }

    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
            return "__missing__";
        }
        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
    }
};

//
// gguf helpers
//

static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
    { LLAMA_ROPE_SCALING_TYPE_NONE,   "none"   },
    { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
    { LLAMA_ROPE_SCALING_TYPE_YARN,   "yarn"   },
};

static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
    for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
        if (kv.second == name) {
            return (llama_rope_scaling_type) kv.first;
        }
    }

    return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
}

static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
    switch (type) {
        case GGUF_TYPE_UINT8:   return std::to_string(((const uint8_t  *)data)[i]);
        case GGUF_TYPE_INT8:    return std::to_string(((const int8_t   *)data)[i]);
        case GGUF_TYPE_UINT16:  return std::to_string(((const uint16_t *)data)[i]);
        case GGUF_TYPE_INT16:   return std::to_string(((const int16_t  *)data)[i]);
        case GGUF_TYPE_UINT32:  return std::to_string(((const uint32_t *)data)[i]);
        case GGUF_TYPE_INT32:   return std::to_string(((const int32_t  *)data)[i]);
        case GGUF_TYPE_UINT64:  return std::to_string(((const uint64_t *)data)[i]);
        case GGUF_TYPE_INT64:   return std::to_string(((const int64_t  *)data)[i]);
        case GGUF_TYPE_FLOAT32: return std::to_string(((const float    *)data)[i]);
        case GGUF_TYPE_FLOAT64: return std::to_string(((const double   *)data)[i]);
        case GGUF_TYPE_BOOL:    return ((const bool *)data)[i] ? "true" : "false";
        default:                return format("unknown type %d", type);
    }
}

static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
    const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);

    switch (type) {
        case GGUF_TYPE_STRING:
            return gguf_get_val_str(ctx_gguf, i);
        case GGUF_TYPE_ARRAY:
            {
                const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
                int arr_n = gguf_get_arr_n(ctx_gguf, i);
                const void * data = gguf_get_arr_data(ctx_gguf, i);
                std::stringstream ss;
                ss << "[";
                for (int j = 0; j < arr_n; j++) {
                    if (arr_type == GGUF_TYPE_STRING) {
                        std::string val = gguf_get_arr_str(ctx_gguf, i, j);
                        // escape quotes
                        replace_all(val, "\\", "\\\\");
                        replace_all(val, "\"", "\\\"");
                        ss << '"' << val << '"';
                    } else if (arr_type == GGUF_TYPE_ARRAY) {
                        ss << "???";
                    } else {
                        ss << gguf_data_to_str(arr_type, data, j);
                    }
                    if (j < arr_n - 1) {
                        ss << ", ";
                    }
                }
                ss << "]";
                return ss.str();
            }
        default:
            return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
    }
}

//
// llama helpers
//

#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
    LPSTR buf;
    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
    if (!size) {
        return "FormatMessageA failed";
    }
    std::string ret(buf, size);
    LocalFree(buf);
    return ret;
}
#endif

template <typename T>
struct no_init {
    T value;
    no_init() { /* do nothing */ }
};

struct llama_file {
    // use FILE * so we don't have to re-open the file to mmap
    FILE * fp;
    size_t size;

    llama_file(const char * fname, const char * mode) {
        fp = std::fopen(fname, mode);
        if (fp == NULL) {
            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
        }
        seek(0, SEEK_END);
        size = tell();
        seek(0, SEEK_SET);
    }

    size_t tell() const {
#ifdef _WIN32
        __int64 ret = _ftelli64(fp);
#else
        long ret = std::ftell(fp);
#endif
        GGML_ASSERT(ret != -1); // this really shouldn't fail
        return (size_t) ret;
    }

    void seek(size_t offset, int whence) const {
#ifdef _WIN32
        int ret = _fseeki64(fp, (__int64) offset, whence);
#else
        int ret = std::fseek(fp, (long) offset, whence);
#endif
        GGML_ASSERT(ret == 0); // same
    }

    void read_raw(void * ptr, size_t len) const {
        if (len == 0) {
            return;
        }
        errno = 0;
        std::size_t ret = std::fread(ptr, len, 1, fp);
        if (ferror(fp)) {
            throw std::runtime_error(format("read error: %s", strerror(errno)));
        }
        if (ret != 1) {
            throw std::runtime_error("unexpectedly reached end of file");
        }
    }

    uint32_t read_u32() const {
        uint32_t ret;
        read_raw(&ret, sizeof(ret));
        return ret;
    }

    void write_raw(const void * ptr, size_t len) const {
        if (len == 0) {
            return;
        }
        errno = 0;
        size_t ret = std::fwrite(ptr, len, 1, fp);
        if (ret != 1) {
            throw std::runtime_error(format("write error: %s", strerror(errno)));
        }
    }

    void write_u32(std::uint32_t val) const {
        write_raw(&val, sizeof(val));
    }

    ~llama_file() {
        if (fp) {
            std::fclose(fp);
        }
    }
};

struct llama_mmap {
    void * addr;
    size_t size;

    llama_mmap(const llama_mmap &) = delete;

#ifdef _POSIX_MAPPED_FILES
    static constexpr bool SUPPORTED = true;

    // list of mapped fragments (first_offset, last_offset)
    std::vector<std::pair<size_t, size_t>> mapped_fragments;

    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
        size = file->size;
        int fd = fileno(file->fp);
        int flags = MAP_SHARED;
        // prefetch/readahead impairs performance on NUMA systems
        if (numa)  { prefetch = 0; }
#ifdef __linux__
        // advise the kernel to read the file sequentially (increases readahead)
        if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
            LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n",
                    strerror(errno));
        }
        if (prefetch) { flags |= MAP_POPULATE; }
#endif
        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
        if (addr == MAP_FAILED) { // NOLINT
            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
        }

        if (prefetch > 0) {
            // advise the kernel to preload the mapped memory
            if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
                LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
                        strerror(errno));
            }
        }
        if (numa) {
            // advise the kernel not to use readahead
            // (because the next page might not belong on the same node)
            if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
                LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
                        strerror(errno));
            }
        }

        // initialize list of mapped_fragments
        mapped_fragments.emplace_back(0, file->size);
    }

    static void align_range(size_t * first, size_t * last, size_t page_size) {
        // align first to the next page
        size_t offset_in_page = *first & (page_size - 1);
        size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page;
        *first += offset_to_page;

        // align last to the previous page
        *last = *last & ~(page_size - 1);

        if (*last <= *first) {
            *last = *first;
        }
    }

    // partially unmap the file in the range [first, last)
    void unmap_fragment(size_t first, size_t last) {
        // note: this function must not be called multiple times with overlapping ranges
        // otherwise, there is a risk of invalidating addresses that have been repurposed for other mappings
        int page_size = sysconf(_SC_PAGESIZE);
        align_range(&first, &last, page_size);
        size_t len = last - first;

        if (len == 0) {
            return;
        }

        GGML_ASSERT(first % page_size == 0);
        GGML_ASSERT(last % page_size == 0);
        GGML_ASSERT(last > first);

        void * next_page_start = (uint8_t *) addr + first;

        // unmap the range
        if (munmap(next_page_start, len)) {
            LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
        }

        // update the list of mapped fragments to avoid unmapping the same range again in the destructor
        std::vector<std::pair<size_t, size_t>> new_mapped_fragments;
        for (const auto & frag : mapped_fragments) {
            if (frag.first < first && frag.second > last) {
                // the range is in the middle of the fragment, split it
                new_mapped_fragments.emplace_back(frag.first, first);
                new_mapped_fragments.emplace_back(last, frag.second);
            } else if (frag.first < first && frag.second > first) {
                // the range starts in the middle of the fragment
                new_mapped_fragments.emplace_back(frag.first, first);
            } else if (frag.first < last && frag.second > last) {
                // the range ends in the middle of the fragment
                new_mapped_fragments.emplace_back(last, frag.second);
            } else if (frag.first >= first && frag.second <= last) {
                // the range covers the entire fragment
            } else {
                // the range is outside the fragment
                new_mapped_fragments.push_back(frag);
            }
        }
        mapped_fragments = std::move(new_mapped_fragments);
    }

    ~llama_mmap() {
        for (const auto & frag : mapped_fragments) {
            if (munmap((char *) addr + frag.first, frag.second - frag.first)) {
                LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
            }
        }
    }
#elif defined(_WIN32)
    static constexpr bool SUPPORTED = true;

    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) {
        GGML_UNUSED(numa);

        size = file->size;

        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));

        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);

        if (hMapping == NULL) {
            DWORD error = GetLastError();
            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
        }

        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
        DWORD error = GetLastError();
        CloseHandle(hMapping);

        if (addr == NULL) {
            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
        }

        if (prefetch > 0) {
#if _WIN32_WINNT >= 0x602
            // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
            BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
            HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");

            // may fail on pre-Windows 8 systems
            pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));

            if (pPrefetchVirtualMemory) {
                // advise the kernel to preload the mapped memory
                WIN32_MEMORY_RANGE_ENTRY range;
                range.VirtualAddress = addr;
                range.NumberOfBytes = (SIZE_T) std::min(size, prefetch);
                if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
                    LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
                            llama_format_win_err(GetLastError()).c_str());
                }
            }
#else
            throw std::runtime_error("PrefetchVirtualMemory unavailable");
#endif
        }
    }

    void unmap_fragment(size_t first, size_t last) {
        // not supported
        GGML_UNUSED(first);
        GGML_UNUSED(last);
    }

    ~llama_mmap() {
        if (!UnmapViewOfFile(addr)) {
            LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
                    llama_format_win_err(GetLastError()).c_str());
        }
    }
#else
    static constexpr bool SUPPORTED = false;

    llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) {
        GGML_UNUSED(file);
        GGML_UNUSED(prefetch);
        GGML_UNUSED(numa);

        throw std::runtime_error("mmap not supported");
    }

    void unmap_fragment(size_t first, size_t last) {
        GGML_UNUSED(first);
        GGML_UNUSED(last);

        throw std::runtime_error("mmap not supported");
    }
#endif
};

// Represents some region of memory being locked using mlock or VirtualLock;
// will automatically unlock on destruction.
struct llama_mlock {
    void * addr = NULL;
    size_t size = 0;

    bool failed_already = false;

    llama_mlock() {}
    llama_mlock(const llama_mlock &) = delete;

    ~llama_mlock() {
        if (size) {
            raw_unlock(addr, size);
        }
    }

    void init(void * ptr) {
        GGML_ASSERT(addr == NULL && size == 0); // NOLINT
        addr = ptr;
    }

    void grow_to(size_t target_size) {
        GGML_ASSERT(addr);
        if (failed_already) {
            return;
        }
        size_t granularity = lock_granularity();
        target_size = (target_size + granularity - 1) & ~(granularity - 1);
        if (target_size > size) {
            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
                size = target_size;
            } else {
                failed_already = true;
            }
        }
    }

#ifdef _POSIX_MEMLOCK_RANGE
    static constexpr bool SUPPORTED = true;

    static size_t lock_granularity() {
        return (size_t) sysconf(_SC_PAGESIZE);
    }

    #ifdef __APPLE__
        #define MLOCK_SUGGESTION \
            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n"
    #else
        #define MLOCK_SUGGESTION \
            "Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n"
    #endif

    bool raw_lock(const void * addr, size_t size) const {
        if (!mlock(addr, size)) {
            return true;
        }

        char* errmsg = std::strerror(errno);
        bool suggest = (errno == ENOMEM);

        // Check if the resource limit is fine after all
        struct rlimit lock_limit;
        if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
            suggest = false;
        }
        if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
            suggest = false;
        }

        LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
                size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
        return false;
    }

    #undef MLOCK_SUGGESTION

    static void raw_unlock(void * addr, size_t size) {
        if (munlock(addr, size)) {
            LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno));
        }
    }
#elif defined(_WIN32)
    static constexpr bool SUPPORTED = true;

    static size_t lock_granularity() {
        SYSTEM_INFO si;
        GetSystemInfo(&si);
        return (size_t) si.dwPageSize;
    }

    bool raw_lock(void * ptr, size_t len) const {
        for (int tries = 1; ; tries++) {
            if (VirtualLock(ptr, len)) {
                return true;
            }
            if (tries == 2) {
                LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
                    len, size, llama_format_win_err(GetLastError()).c_str());
                return false;
            }

            // It failed but this was only the first try; increase the working
            // set size and try again.
            SIZE_T min_ws_size, max_ws_size;
            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
                LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n",
                        llama_format_win_err(GetLastError()).c_str());
                return false;
            }
            // Per MSDN: "The maximum number of pages that a process can lock
            // is equal to the number of pages in its minimum working set minus
            // a small overhead."
            // Hopefully a megabyte is enough overhead:
            size_t increment = len + 1048576;
            // The minimum must be <= the maximum, so we need to increase both:
            min_ws_size += increment;
            max_ws_size += increment;
            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
                LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n",
                        llama_format_win_err(GetLastError()).c_str());
                return false;
            }
        }
    }

    static void raw_unlock(void * ptr, size_t len) {
        if (!VirtualUnlock(ptr, len)) {
            LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n",
                    llama_format_win_err(GetLastError()).c_str());
        }
    }
#else
    static constexpr bool SUPPORTED = false;

    static size_t lock_granularity() {
        return (size_t) 65536;
    }

    bool raw_lock(const void * addr, size_t len) const {
        LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
        return false;
    }

    static void raw_unlock(const void * addr, size_t len) {}
#endif
};

static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
    std::vector<char> result(8, 0);
    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
    if (n_tokens < 0) {
        result.resize(-n_tokens);
        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
        GGML_ASSERT(check == -n_tokens);
    }
    else {
        result.resize(n_tokens);
    }

    return std::string(result.data(), result.size());
}

static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
    ggml_backend_buffer_type_t buft = nullptr;

#if defined(GGML_USE_CUBLAS)
    // host buffers should only be used when data is expected to be copied to/from the GPU
    if (host_buffer) {
        buft = ggml_backend_cuda_host_buffer_type();
    }
#elif defined(GGML_USE_SYCL)
    if (host_buffer) {
        buft = ggml_backend_sycl_host_buffer_type();
    }
#elif defined(GGML_USE_CPU_HBM)
    buft = ggml_backend_cpu_hbm_buffer_type();
#elif defined(GGML_USE_VULKAN)
    if (host_buffer) {
        buft = ggml_backend_vk_host_buffer_type();
    }
#endif

    if (buft == nullptr) {
        buft = ggml_backend_cpu_buffer_type();
    }
    return buft;

    GGML_UNUSED(host_buffer);
}

static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
    ggml_backend_buffer_type_t buft = nullptr;

#ifdef GGML_USE_METAL
    buft = ggml_backend_metal_buffer_type();
#elif defined(GGML_USE_CUBLAS)
    buft = ggml_backend_cuda_buffer_type(gpu);
#elif defined(GGML_USE_VULKAN)
    buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
    buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
    buft = ggml_backend_opencl_buffer_type();
#elif defined(GGML_USE_KOMPUTE)
    buft = ggml_backend_kompute_buffer_type(gpu);
    if (buft == nullptr) {
        LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
    }
#endif

    if (buft == nullptr) {
        buft = llama_default_buffer_type_cpu(true);
    }
    return buft;

    GGML_UNUSED(gpu);
}

static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
    ggml_backend_buffer_type_t buft = nullptr;

#ifdef GGML_USE_CUBLAS
    if (ggml_backend_cuda_get_device_count() > 1) {
        buft = ggml_backend_cuda_split_buffer_type(tensor_split);
    }
#endif

#ifdef GGML_USE_SYCL
    if (ggml_backend_sycl_get_device_count() > 1) {
        buft = ggml_backend_sycl_split_buffer_type(tensor_split);
    }
#endif

    if (buft == nullptr) {
        buft = llama_default_buffer_type_offload(fallback_gpu);
    }
    return buft;

    GGML_UNUSED(tensor_split);
}

static size_t llama_get_device_count() {
#if defined(GGML_USE_CUBLAS)
    return ggml_backend_cuda_get_device_count();
#elif defined(GGML_USE_SYCL)
    return ggml_backend_sycl_get_device_count();
#elif defined(GGML_USE_VULKAN)
    return ggml_backend_vk_get_device_count();
#else
    return 1;
#endif
}

static size_t llama_get_device_memory(int device) {
#if defined(GGML_USE_CUBLAS)
    size_t total;
    size_t free;
    ggml_backend_cuda_get_device_memory(device, &total, &free);
    return free;
#elif defined(GGML_USE_SYCL)
    size_t total;
    size_t free;
    ggml_backend_sycl_get_device_memory(device, &total, &free);
    return free;
#elif defined(GGML_USE_VULKAN)
    size_t total;
    size_t free;
    ggml_backend_vk_get_device_memory(device, &total, &free);
    return free;
#else
    return 1;
    GGML_UNUSED(device);
#endif
}

//
// globals
//

struct llama_state {
    llama_state() {
#ifdef GGML_USE_METAL
        ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
#endif
    }

    // We save the log callback globally
    ggml_log_callback log_callback = llama_log_callback_default;
    void * log_callback_user_data = nullptr;
};

static llama_state g_state;

// available llama models
enum e_model {
    MODEL_UNKNOWN,
    MODEL_17M,
    MODEL_22M,
    MODEL_33M,
    MODEL_109M,
    MODEL_137M,
    MODEL_335M,
    MODEL_0_5B,
    MODEL_1B,
    MODEL_2B,
    MODEL_3B,
    MODEL_4B,
    MODEL_7B,
    MODEL_8B,
    MODEL_13B,
    MODEL_14B,
    MODEL_15B,
    MODEL_20B,
    MODEL_30B,
    MODEL_34B,
    MODEL_40B,
    MODEL_65B,
    MODEL_70B,
    MODEL_SMALL,
    MODEL_MEDIUM,
    MODEL_LARGE,
    MODEL_XL,
};

static const size_t kiB = 1024;
static const size_t MiB = 1024*kiB;
static const size_t GiB = 1024*MiB;

struct llama_hparams {
    bool vocab_only;
    bool rope_finetuned;

    uint32_t n_vocab;
    uint32_t n_ctx_train; // context size the model was trained on
    uint32_t n_embd;
    uint32_t n_head;
    uint32_t n_head_kv;
    uint32_t n_layer;
    uint32_t n_rot;
    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
    uint32_t n_ff;
    uint32_t n_expert = 0;
    uint32_t n_expert_used = 0;
    uint32_t n_vocab_type = 0; // for BERT-style token types

    float f_norm_eps;
    float f_norm_rms_eps;

    float    rope_freq_base_train;
    float    rope_freq_scale_train;
    uint32_t n_yarn_orig_ctx;

    // for State Space Models
    uint32_t ssm_d_conv  = 0;
    uint32_t ssm_d_inner = 0;
    uint32_t ssm_d_state = 0;
    uint32_t ssm_dt_rank = 0;

    float f_clamp_kqv      = 0.0f;
    float f_max_alibi_bias = 0.0f;

    bool causal_attn = true;
    bool need_kq_pos = false;

    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;

    bool operator!=(const llama_hparams & other) const {
        if (this->vocab_only    != other.vocab_only)    return true;
        if (this->n_vocab       != other.n_vocab)       return true;
        if (this->n_ctx_train   != other.n_ctx_train)   return true;
        if (this->n_embd        != other.n_embd)        return true;
        if (this->n_head        != other.n_head)        return true;
        if (this->n_head_kv     != other.n_head_kv)     return true;
        if (this->n_layer       != other.n_layer)       return true;
        if (this->n_rot         != other.n_rot)         return true;
        if (this->n_embd_head_k != other.n_embd_head_k) return true;
        if (this->n_embd_head_v != other.n_embd_head_v) return true;
        if (this->n_ff          != other.n_ff)          return true;
        if (this->n_expert      != other.n_expert)      return true;
        if (this->n_expert_used != other.n_expert_used) return true;

        if (this->rope_finetuned  != other.rope_finetuned)  return true;
        if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;

        if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
        if (this->ssm_d_inner != other.ssm_d_inner) return true;
        if (this->ssm_d_state != other.ssm_d_state) return true;
        if (this->ssm_dt_rank != other.ssm_dt_rank) return true;

        const float EPSILON = 1e-9f;

        if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
        if (!is_float_close(this->f_norm_rms_eps,        other.f_norm_rms_eps,        EPSILON)) return true;
        if (!is_float_close(this->rope_freq_base_train,  other.rope_freq_base_train,  EPSILON)) return true;
        if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;

        return false;
    }

    uint32_t n_gqa() const {
        if (n_head_kv == 0) {
            return 0;
        }
        return n_head/n_head_kv;
    }

    uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads
        return n_embd_head_k * n_head_kv;
    }

    uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
        return n_embd_head_v * n_head_kv;
    }

    uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
        // corresponds to Mamba's conv_states size
        // TODO: maybe support other convolution strides than 1
        // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
        return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
    }

    uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
        // corresponds to Mamba's ssm_states size
        return ssm_d_state * ssm_d_inner;
    }
};

struct llama_cparams {
    uint32_t n_ctx;           // context size used during inference
    uint32_t n_batch;
    uint32_t n_ubatch;
    uint32_t n_threads;       // number of threads to use for generation
    uint32_t n_threads_batch; // number of threads to use for batch processing

    float rope_freq_base;
    float rope_freq_scale;

    uint32_t n_yarn_orig_ctx;
    // These hyperparameters are not exposed in GGUF, because all
    // existing YaRN models use the same values for them.
    float yarn_ext_factor;
    float yarn_attn_factor;
    float yarn_beta_fast;
    float yarn_beta_slow;
    float defrag_thold;

    bool embeddings;
    bool causal_attn;
    bool offload_kqv;

    enum llama_pooling_type pooling_type;

    ggml_backend_sched_eval_callback cb_eval;
    void * cb_eval_user_data;
};

struct llama_layer {
    // normalization
    struct ggml_tensor * attn_norm;
    struct ggml_tensor * attn_norm_b;
    struct ggml_tensor * attn_norm_2;
    struct ggml_tensor * attn_norm_2_b;
    struct ggml_tensor * attn_q_norm;
    struct ggml_tensor * attn_q_norm_b;
    struct ggml_tensor * attn_k_norm;
    struct ggml_tensor * attn_k_norm_b;
    struct ggml_tensor * attn_out_norm;
    struct ggml_tensor * attn_out_norm_b;

    // attention
    struct ggml_tensor * wq;
    struct ggml_tensor * wk;
    struct ggml_tensor * wv;
    struct ggml_tensor * wo;
    struct ggml_tensor * wqkv;

    // attention bias
    struct ggml_tensor * bq;
    struct ggml_tensor * bk;
    struct ggml_tensor * bv;
    struct ggml_tensor * bo;
    struct ggml_tensor * bqkv;

    // normalization
    struct ggml_tensor * ffn_norm;
    struct ggml_tensor * ffn_norm_b;
    struct ggml_tensor * layer_out_norm;
    struct ggml_tensor * layer_out_norm_b;

    // ff
    struct ggml_tensor * ffn_gate; // w1
    struct ggml_tensor * ffn_down; // w2
    struct ggml_tensor * ffn_up;   // w3

    // ff MoE
    struct ggml_tensor * ffn_gate_inp;
    struct ggml_tensor * ffn_gate_exp[LLAMA_MAX_EXPERTS];
    struct ggml_tensor * ffn_down_exp[LLAMA_MAX_EXPERTS];
    struct ggml_tensor * ffn_up_exp  [LLAMA_MAX_EXPERTS];

    // ff bias
    struct ggml_tensor * ffn_down_b; // b2
    struct ggml_tensor * ffn_up_b;   // b3
    struct ggml_tensor * ffn_act;

    // mamba proj
    struct ggml_tensor * ssm_in;
    struct ggml_tensor * ssm_x;
    struct ggml_tensor * ssm_dt;
    struct ggml_tensor * ssm_out;

    // mamba
    struct ggml_tensor * ssm_conv1d;
    struct ggml_tensor * ssm_a;
    struct ggml_tensor * ssm_d;

    // mamba bias
    struct ggml_tensor * ssm_conv1d_b;
    struct ggml_tensor * ssm_dt_b;
};

struct llama_kv_cell {
    llama_pos pos   = -1;
    llama_pos delta = 0;
    int32_t   src   = 0; // used by recurrent state models to copy states

    std::set<llama_seq_id> seq_id;

    bool has_seq_id(const llama_seq_id & id) const {
        return seq_id.find(id) != seq_id.end();
    }

    bool is_empty() const {
        return seq_id.empty();
    }

    bool is_same_seq(const llama_kv_cell & other) const {
        return seq_id == other.seq_id;
    }
};

// ring-buffer of cached KV data
struct llama_kv_cache {
    bool has_shift = false;
    bool do_defrag = false;
    bool do_copy   = false;
    // with recurrent state models, a cell can hold the state for more than one past token
    bool recurrent = false;

    // Note: The value of head isn't only used to optimize searching
    // for a free KV slot. llama_decode_internal also uses it, so it
    // cannot be freely changed after a slot has been allocated.
    uint32_t head = 0;
    uint32_t size = 0;
    uint32_t used = 0; // used cells (i.e. at least one seq_id)

    // computed before each graph build
    uint32_t n = 0;

    ggml_type type_k = GGML_TYPE_F16;
    ggml_type type_v = GGML_TYPE_F16;

    std::vector<llama_kv_cell> cells;

    std::vector<struct ggml_tensor *> k_l; // per layer
    std::vector<struct ggml_tensor *> v_l;

    std::vector<struct ggml_context *> ctxs;
    std::vector<ggml_backend_buffer_t> bufs;

    size_t total_size() const {
        size_t size = 0;
        for (ggml_backend_buffer_t buf : bufs) {
            size += ggml_backend_buffer_get_size(buf);
        }
        return size;
    }

    ~llama_kv_cache() {
        for (struct ggml_context * ctx : ctxs) {
            ggml_free(ctx);
        }
        for (ggml_backend_buffer_t buf : bufs) {
            ggml_backend_buffer_free(buf);
        }
    }
};

struct llama_vocab {
    using id    = int32_t;
    using token = std::string;
    using ttype = llama_token_type;

    struct token_data {
        token text;
        float score;
        ttype type;
    };

    enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;

    std::unordered_map<token, id> token_to_id;
    std::vector<token_data>       id_to_token;

    std::unordered_map<token, id> special_tokens_cache;

    std::map<std::pair<std::string, std::string>, int> bpe_ranks;

    // default LLaMA special tokens
    id special_bos_id = 1;
    id special_eos_id = 2;
    id special_unk_id = 0;
    id special_sep_id = -1;
    id special_pad_id = -1;

    int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
    int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.

    id linefeed_id       = 13;
    id special_prefix_id = 32007;
    id special_middle_id = 32009;
    id special_suffix_id = 32008;
    id special_eot_id    = 32010;

    bool add_space_prefix = true;

    int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
        GGML_ASSERT(token_left.find(' ') == std::string::npos);
        GGML_ASSERT(token_left.find('\n') == std::string::npos);
        GGML_ASSERT(token_right.find(' ') == std::string::npos);
        GGML_ASSERT(token_right.find('\n') == std::string::npos);

        auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
        if (it == bpe_ranks.end()) {
            return -1;
        }

        return it->second;
    }
};

struct llama_model {
    e_model     type  = MODEL_UNKNOWN;
    llm_arch    arch  = LLM_ARCH_UNKNOWN;
    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;

    std::string name = "n/a";

    llama_hparams hparams = {};
    llama_vocab   vocab;

    struct ggml_tensor * tok_embd;
    struct ggml_tensor * type_embd;
    struct ggml_tensor * pos_embd;
    struct ggml_tensor * tok_norm;
    struct ggml_tensor * tok_norm_b;

    struct ggml_tensor * output_norm;
    struct ggml_tensor * output_norm_b;
    struct ggml_tensor * output;
    struct ggml_tensor * output_b;

    std::vector<llama_layer> layers;

    llama_split_mode split_mode;
    int main_gpu;
    int n_gpu_layers;

    // gguf metadata
    std::unordered_map<std::string, std::string> gguf_kv;

    // layer -> buffer type mapping
    struct layer_buft {
        layer_buft() : buft_matrix(nullptr), buft(nullptr) {}
        layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {}
        layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {}

        ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication
        ggml_backend_buffer_type_t buft;        // everything else
    };

    layer_buft buft_input;
    layer_buft buft_output;
    std::vector<layer_buft> buft_layer;

    // contexts where the model tensors metadata is stored
    std::vector<struct ggml_context *> ctxs;

    // the model memory buffers for the tensor data
    std::vector<ggml_backend_buffer_t> bufs;

    // model memory mapped file
    std::unique_ptr<llama_mmap> mapping;

    // objects representing data potentially being locked in memory
    std::vector<std::unique_ptr<llama_mlock>> mlock_bufs;
    llama_mlock mlock_mmap;

    // for quantize-stats only
    std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;

    int64_t t_load_us = 0;
    int64_t t_start_us = 0;

    ~llama_model() {
        for (struct ggml_context * ctx : ctxs) {
            ggml_free(ctx);
        }
        for (ggml_backend_buffer_t buf : bufs) {
            ggml_backend_buffer_free(buf);
        }
    }
};

struct llama_context {
    llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
    ~llama_context() {
        ggml_backend_sched_free(sched);

        for (ggml_backend_t backend : backends) {
            ggml_backend_free(backend);
        }

#ifdef GGML_USE_VULKAN
        ggml_vk_free_cpu_assist();
#endif

        ggml_backend_buffer_free(buf_output);
    }

    llama_cparams cparams;

    std::vector<ggml_backend_t> backends;
#ifdef GGML_USE_METAL
    ggml_backend_t backend_metal = nullptr;
#endif
    ggml_backend_t backend_cpu = nullptr;

    const llama_model & model;

    // key + value cache for the self attention
    struct llama_kv_cache kv_self;

    std::mt19937 rng;

    bool has_evaluated_once = false;

    int64_t t_start_us;
    int64_t t_load_us;
    int64_t t_sample_us = 0;
    int64_t t_p_eval_us = 0;
    int64_t t_eval_us   = 0;

    int64_t t_compute_start_us = 0;
    int64_t n_queued_tokens = 0;

    int32_t n_sample = 0; // number of tokens sampled
    int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
    int32_t n_eval   = 0; // number of eval calls

    // host buffer for the model output (logits and embeddings)
    ggml_backend_buffer_t buf_output = nullptr;

    // decode output (2-dimensional array: [n_tokens][n_vocab])
    size_t logits_size = 0;
    float * logits = nullptr;

#ifndef NDEBUG
    // guard against access to unset logits
    std::vector<bool>  logits_valid;
#endif
    bool logits_all = false;

    // embeddings output (2-dimensional array: [n_tokens][n_embd])
    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
    size_t embd_size = 0;
    float * embd = nullptr;

    // sequence embeddings output (map of [n_embd] vectors)
    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
    std::map<llama_seq_id, std::vector<float>> embd_seq;

    // memory buffers used to evaluate the model
    std::vector<uint8_t> buf_compute_meta;
    ggml_backend_sched_t sched = nullptr;

    ggml_abort_callback abort_callback      = nullptr;
    void *              abort_callback_data = nullptr;

    // input tensors
    struct ggml_tensor * inp_tokens;    // I32 [n_batch]
    struct ggml_tensor * inp_embd;      // F32 [n_embd, n_batch]
    struct ggml_tensor * inp_pos;       // I32 [n_batch]
    struct ggml_tensor * inp_KQ_mask;   // F32 [kv_size, n_batch]
    struct ggml_tensor * inp_KQ_pos;    // F32 [kv_size]
    struct ggml_tensor * inp_K_shift;   // I32 [kv_size]
    struct ggml_tensor * inp_mean;      // F32 [n_batch, n_batch]
    struct ggml_tensor * inp_cls;       // I32 [n_batch]
    struct ggml_tensor * inp_s_copy;    // I32 [kv_size]
    struct ggml_tensor * inp_s_mask;    // F32 [1, kv_size]
    struct ggml_tensor * inp_s_seq;     // I32 [kv_size, n_batch]

#ifdef GGML_USE_MPI
    ggml_mpi_context * ctx_mpi = NULL;
#endif
};

//
// kv cache helpers
//

static bool llama_kv_cache_init(
             struct llama_kv_cache & cache,
                 const llama_model & model,
                         ggml_type   type_k,
                         ggml_type   type_v,
                          uint32_t   kv_size,
                              bool   offload) {
    const struct llama_hparams & hparams = model.hparams;

    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
    const int64_t  n_layer      = hparams.n_layer;

    cache.has_shift = false;

    // TODO: find a nicer way to add other recurrent model architectures
    cache.recurrent = model.arch == LLM_ARCH_MAMBA;

    // TODO: support mixed reccurent Transformer architectues
    // NOTE: (!a || b) is a logical implication (a -> b)
    GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
    GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());
    GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa());
    GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa());

    cache.head = 0;
    cache.size = kv_size;
    cache.used = 0;

    cache.type_k = type_k;
    cache.type_v = type_v;

    cache.cells.clear();
    cache.cells.resize(kv_size);

    if (cache.recurrent) {
        // init state copy sources
        for (uint32_t i = 0; i < cache.size; ++i) {
            cache.cells[i].src = i;
        }
    }

#ifdef GGML_USE_CLBLAST
    offload = false;
#endif

    // count used buffer types
    std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
    if (offload) {
        for (int64_t i = 0; i < n_layer; ++i) {
            buft_layer_count[model.buft_layer[i].buft]++;
        }
    } else {
        buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer;
    }

    // create a context for each buffer type
    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
    for (auto & it : buft_layer_count) {
        int n_layers = it.second;
        struct ggml_init_params params = {
            /*.mem_size   =*/ 2u*n_layers*ggml_tensor_overhead(),
            /*.mem_buffer =*/ NULL,
            /*.no_alloc   =*/ true,
        };
        ggml_context * ctx = ggml_init(params);
        if (!ctx) {
            LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__);
            return false;
        }
        ctx_map[it.first] = ctx;
        cache.ctxs.push_back(ctx);
    }

    cache.k_l.reserve(n_layer);
    cache.v_l.reserve(n_layer);

    for (int i = 0; i < (int) n_layer; i++) {
        struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
        ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
        ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
        ggml_format_name(k, "cache_k_l%d", i);
        ggml_format_name(v, "cache_v_l%d", i);
        cache.k_l.push_back(k);
        cache.v_l.push_back(v);
    }

    // allocate tensors and initialize the buffers to avoid NaNs in the padding
    for (auto it : ctx_map) {
        ggml_backend_buffer_type_t buft = it.first;
        ggml_context * ctx = it.second;
        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
        if (!buf) {
            LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
            return false;
        }
        ggml_backend_buffer_clear(buf, 0);
        LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
        cache.bufs.push_back(buf);
    }

    return true;
}

// find an empty slot of size "n_tokens" in the cache
// updates the cache head
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
static bool llama_kv_cache_find_slot(
           struct llama_kv_cache & cache,
        const struct llama_batch & batch) {
    const uint32_t n_ctx    = cache.size;
    const uint32_t n_tokens = batch.n_tokens;

    if (cache.recurrent) {
        // For recurrent state architectures (like Mamba),
        // each KV cache cell can store the state for a whole sequence.

        llama_seq_id min = cache.size - 1;
        llama_seq_id max = 0;

        for (uint32_t i = 0; i < n_tokens; ++i) {
            for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) {
                llama_seq_id seq_id = batch.seq_id[i][j];
                // make sure it's a valid seq_id
                if ((uint32_t) seq_id < cache.size) {
                    if (seq_id > max) {
                        max = seq_id;
                    }
                    if (seq_id < min) {
                        min = seq_id;
                    }
                    // Assuming the tokens are in-order
                    if (batch.pos[i] != cache.cells[seq_id].pos + 1) {
                        // What should happen when the pos backtracks or skips a value?
                        // Clearing the state mid-batch would require special-casing which isn't done.
                        LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n",
                            __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id);
                    }
                    if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) {
                        cache.used += 1;
                    }
                    cache.cells[seq_id].pos = batch.pos[i];
                    // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set
                } else {
                    // too big seq_id
                    // TODO: would it be possible to resize the KV cache size instead?
                    LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
                    return false;
                }
            }
        }

        // allow getting the range of used cells, from head to head + n
        cache.head = min;
        cache.n    = max - min + 1;

        // sanity check
        return max >= min;
    }
    // otherwise, one cell per token.

    if (n_tokens > n_ctx) {
        LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
        return false;
    }

    uint32_t n_tested = 0;

    while (true) {
        if (cache.head + n_tokens > n_ctx) {
            n_tested += n_ctx - cache.head;
            cache.head = 0;
            continue;
        }

        bool found = true;
        for (uint32_t i = 0; i < n_tokens; i++) {
            if (cache.cells[cache.head + i].pos >= 0) {
                found = false;
                cache.head += i + 1;
                n_tested   += i + 1;
                break;
            }
        }

        if (found) {
            break;
        }

        if (n_tested >= n_ctx) {
            //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
            return false;
        }
    }

    for (uint32_t i = 0; i < n_tokens; i++) {
        cache.cells[cache.head + i].pos = batch.pos[i];

        for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
            cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
        }
    }

    cache.used += n_tokens;

    return true;
}

// find how many cells are currently in use
static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
    for (uint32_t i = cache.size; i > 0; --i) {
        const llama_kv_cell & cell = cache.cells[i - 1];

        if (cell.pos >= 0 && !cell.is_empty()) {
            return i;
        }
    }

    return 0;
}

static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
    for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
        cache.cells[i].pos = -1;
        cache.cells[i].seq_id.clear();
    }
    cache.head = 0;
    cache.used = 0;
}

static bool llama_kv_cache_seq_rm(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id,
                    llama_pos   p0,
                    llama_pos   p1) {
    uint32_t new_head = cache.size;

    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    // models like Mamba can't have a state partially erased
    if (cache.recurrent) {
        if (seq_id >= (int64_t) cache.size) {
            // could be fatal
            return false;
        }
        if (0 <= seq_id) {
            // partial intersection is invalid
            if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) {
                return false;
            }
        } else {
            // seq_id is negative, then the range should include everything or nothing
            if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
                return false;
            }
        }
    }

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            if (seq_id < 0) {
                cache.cells[i].seq_id.clear();
            } else if (cache.cells[i].has_seq_id(seq_id)) {
                cache.cells[i].seq_id.erase(seq_id);
            } else {
                continue;
            }
            if (cache.cells[i].is_empty()) {
                // keep count of the number of used cells
                if (cache.cells[i].pos >= 0) cache.used--;

                cache.cells[i].pos = -1;
                if (new_head == cache.size) new_head = i;
            }
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
    if (new_head != cache.size && new_head < cache.head) cache.head = new_head;

    return true;
}

static void llama_kv_cache_seq_cp(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id_src,
                 llama_seq_id   seq_id_dst,
                    llama_pos   p0,
                    llama_pos   p1) {
    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    if (cache.recurrent) {
        if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
            seq_id_src = cache.cells[seq_id_src].src;
            GGML_ASSERT((uint32_t) seq_id_src < cache.size);
            // intent to "copy from"
            // supports copy chains thanks to taking the source of the source
            cache.cells[seq_id_dst].src = seq_id_src;

            // preserve the "keep or clear" status of the copied sequence
            if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) {
                cache.cells[seq_id_dst].seq_id.insert(seq_id_dst);
            } else {
                cache.cells[seq_id_dst].seq_id.erase(seq_id_dst);
            }

            cache.do_copy = true;

            cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos;
        }
        return;
    }
    // otherwise, this is the KV cache of a Transformer-like model

    cache.head = 0;

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            cache.cells[i].seq_id.insert(seq_id_dst);
        }
    }
}

static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
    uint32_t new_head = cache.size;

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (!cache.cells[i].has_seq_id(seq_id)) {
            if (cache.cells[i].pos >= 0) cache.used--;
            cache.cells[i].pos = -1;
            cache.cells[i].seq_id.clear();
            if (new_head == cache.size) new_head = i;
        } else {
            cache.cells[i].seq_id.clear();
            cache.cells[i].seq_id.insert(seq_id);
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
    if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
}

static void llama_kv_cache_seq_add(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id,
                    llama_pos   p0,
                    llama_pos   p1,
                    llama_pos   delta) {
    uint32_t new_head = cache.size;

    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    if (cache.recurrent) {
        // for Mamba-like models, only the pos needs to be shifted
        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
            llama_kv_cell & cell = cache.cells[seq_id];
            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
                cell.pos += delta;
            }
        }
        return;
    }

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            cache.has_shift = true;
            cache.cells[i].pos   += delta;
            cache.cells[i].delta += delta;

            if (cache.cells[i].pos < 0) {
                if (!cache.cells[i].is_empty()) {
                    cache.used--;
                }
                cache.cells[i].pos = -1;
                cache.cells[i].seq_id.clear();
                if (new_head == cache.size) {
                    new_head = i;
                }
            }
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
    // Otherwise we just start the next search from the beginning.
    cache.head = new_head != cache.size ? new_head : 0;
}

static void llama_kv_cache_seq_div(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id,
                    llama_pos   p0,
                    llama_pos   p1,
                          int   d) {
    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    if (cache.recurrent) {
        // for Mamba-like models, only the pos needs to be changed
        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
            llama_kv_cell & cell = cache.cells[seq_id];
            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
                cell.pos /= d;
            }
        }
        return;
    }

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            cache.has_shift = true;

            {
                llama_pos p_old = cache.cells[i].pos;
                cache.cells[i].pos   /= d;
                cache.cells[i].delta += cache.cells[i].pos - p_old;
            }
        }
    }
}

static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
    llama_pos result = 0;

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].has_seq_id(seq_id)) {
            result = std::max(result, cache.cells[i].pos);
        }
    }

    return result;
}

static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
    cache.do_defrag = true;
}

//
// model loading and saving
//

enum llama_fver {
    GGUF_FILE_VERSION_V1 = 1,
    GGUF_FILE_VERSION_V2 = 2,
    GGUF_FILE_VERSION_V3 = 3,
};

static const char * llama_file_version_name(llama_fver version) {
    switch (version) {
        case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
        case GGUF_FILE_VERSION_V2: return "GGUF V2";
        case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
    }

    return "unknown";
}

static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
    char buf[256];
    snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
    for (size_t i = 1; i < ne.size(); i++) {
        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
    }
    return buf;
}

static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
    char buf[256];
    snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
    for (int i = 1; i < GGML_MAX_DIMS; i++) {
        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
    }
    return buf;
}

namespace GGUFMeta {
    template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
    struct GKV_Base_Type {
        static constexpr gguf_type gt = gt_;

        static T getter(const gguf_context * ctx, const int kid) {
            return gfun(ctx, kid);
        }
    };

    template<typename T> struct GKV_Base;

    template<> struct GKV_Base<bool        >: GKV_Base_Type<bool,         GGUF_TYPE_BOOL,    gguf_get_val_bool> {};
    template<> struct GKV_Base<uint8_t     >: GKV_Base_Type<uint8_t,      GGUF_TYPE_UINT8,   gguf_get_val_u8  > {};
    template<> struct GKV_Base<uint16_t    >: GKV_Base_Type<uint16_t,     GGUF_TYPE_UINT16,  gguf_get_val_u16 > {};
    template<> struct GKV_Base<uint32_t    >: GKV_Base_Type<uint32_t,     GGUF_TYPE_UINT32,  gguf_get_val_u32 > {};
    template<> struct GKV_Base<uint64_t    >: GKV_Base_Type<uint64_t,     GGUF_TYPE_UINT64,  gguf_get_val_u64 > {};
    template<> struct GKV_Base<int8_t      >: GKV_Base_Type<int8_t,       GGUF_TYPE_INT8,    gguf_get_val_i8  > {};
    template<> struct GKV_Base<int16_t     >: GKV_Base_Type<int16_t,      GGUF_TYPE_INT16,   gguf_get_val_i16 > {};
    template<> struct GKV_Base<int32_t     >: GKV_Base_Type<int32_t,      GGUF_TYPE_INT32,   gguf_get_val_i32 > {};
    template<> struct GKV_Base<int64_t     >: GKV_Base_Type<int64_t,      GGUF_TYPE_INT64,   gguf_get_val_i64 > {};
    template<> struct GKV_Base<float       >: GKV_Base_Type<float,        GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
    template<> struct GKV_Base<double      >: GKV_Base_Type<double,       GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
    template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING,  gguf_get_val_str > {};

    template<> struct GKV_Base<std::string> {
        static constexpr gguf_type gt = GGUF_TYPE_STRING;

        static std::string getter(const gguf_context * ctx, const int kid) {
            return gguf_get_val_str(ctx, kid);
        }
    };

    struct ArrayInfo {
        const gguf_type gt;
        const size_t length;
        const void * data;
    };

    template<> struct GKV_Base<ArrayInfo> {
        public:
        static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
        static ArrayInfo getter(const gguf_context *ctx, const int k) {
            return ArrayInfo {
                gguf_get_arr_type(ctx, k),
                size_t(gguf_get_arr_n(ctx, k)),
                gguf_get_arr_data(ctx, k),
            };
        }
    };

    template<typename T>
    class GKV : public GKV_Base<T> {
        GKV() = delete;

        public:
        static T get_kv(const gguf_context * ctx, const int k) {
            const enum gguf_type kt = gguf_get_kv_type(ctx, k);

            if (kt != GKV::gt) {
                throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
                    gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
            }
            return GKV::getter(ctx, k);
        }

        static const char * override_type_to_str(const llama_model_kv_override_type ty) {
            switch (ty) {
                case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
                case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
                case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
            }
            return "unknown";
        }

        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
            if (!ovrd) { return false; }
            if (ovrd->tag == expected_type) {
                LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
                    __func__, override_type_to_str(ovrd->tag), ovrd->key);
                switch (ovrd->tag) {
                    case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
                        LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false");
                    } break;
                    case LLAMA_KV_OVERRIDE_TYPE_INT:   {
                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value);
                    } break;
                    case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
                        LLAMA_LOG_INFO("%.6f\n", ovrd->float_value);
                    } break;
                    default:
                        // Shouldn't be possible to end up here, but just in case...
                        throw std::runtime_error(
                            format("Unsupported attempt to override %s type for metadata key %s\n",
                                override_type_to_str(ovrd->tag), ovrd->key));
                }
                return true;
            }
            LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
                __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
            return false;
        }

        template<typename OT>
        static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
                target = ovrd->bool_value;
                return true;
            }
            return false;
        }

        template<typename OT>
        static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
                target = ovrd->int_value;
                return true;
            }
            return false;
        }

        template<typename OT>
        static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
        try_override(T & target, const struct llama_model_kv_override * ovrd) {
            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
                target = ovrd->float_value;
                return true;
            }
            return false;
        }

        template<typename OT>
        static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
        try_override(T & target, const struct llama_model_kv_override * ovrd) {
            (void)target;
            (void)ovrd;
            if (!ovrd) { return false; }
            // Currently, we should never end up here so it would be a bug if we do.
            throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
                ovrd ? ovrd->key : "NULL"));
        }

        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
            if (try_override<T>(target, ovrd)) {
                return true;
            }
            if (k < 0) { return false; }
            target = get_kv(ctx, k);
            return true;
        }

        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
            return set(ctx, gguf_find_key(ctx, key), target, ovrd);
        }

        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
            return set(ctx, key.c_str(), target, ovrd);
        }
    };
}

struct llama_model_loader {
    int n_kv      = 0;
    int n_tensors = 0;
    int n_created = 0;

    int64_t n_elements = 0;
    size_t  n_bytes    = 0;

    bool use_mmap = false;

    llama_file  file;
    llama_ftype ftype;
    llama_fver  fver;

    std::unique_ptr<llama_mmap> mapping;
    std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;

    struct gguf_context * ctx_gguf = NULL;
    struct ggml_context * ctx_meta = NULL;

    std::string arch_name;
    LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);

    llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") {
        int trace = 0;
        if (getenv("LLAMA_TRACE")) {
            trace = atoi(getenv("LLAMA_TRACE"));
        }

        struct gguf_init_params params = {
            /*.no_alloc = */ true,
            /*.ctx      = */ &ctx_meta,
        };

        if (param_overrides_p != nullptr) {
            for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
                kv_overrides.insert({std::string(p->key), *p});
            }
        }

        ctx_gguf = gguf_init_from_file(fname.c_str(), params);
        if (!ctx_gguf) {
            throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
        }

        get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
        llm_kv = LLM_KV(llm_arch_from_string(arch_name));

        n_kv      = gguf_get_n_kv(ctx_gguf);
        n_tensors = gguf_get_n_tensors(ctx_gguf);

        fver = (enum llama_fver ) gguf_get_version(ctx_gguf);

        for (int i = 0; i < n_tensors; i++) {
            const char * name = gguf_get_tensor_name(ctx_gguf, i);
            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
            n_elements += ggml_nelements(t);
            n_bytes    += ggml_nbytes(t);
        }

        LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
                __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));

        // determine file type based on the number of tensors for each quantization and print meta data
        // TODO: make optional
        {
            std::map<enum ggml_type, uint32_t> n_type;

            uint32_t n_type_max = 0;
            enum ggml_type type_max = GGML_TYPE_F32;

            for (int i = 0; i < n_tensors; i++) {
                enum ggml_type type = gguf_get_tensor_type(ctx_gguf, i);

                n_type[type]++;

                if (n_type_max < n_type[type]) {
                    n_type_max = n_type[type];
                    type_max   = type;
                }

                if (trace > 0) {
                    struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
                    LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
                }
            }

            switch (type_max) {
                case GGML_TYPE_F32:     ftype = LLAMA_FTYPE_ALL_F32;        break;
                case GGML_TYPE_F16:     ftype = LLAMA_FTYPE_MOSTLY_F16;     break;
                case GGML_TYPE_Q4_0:    ftype = LLAMA_FTYPE_MOSTLY_Q4_0;    break;
                case GGML_TYPE_Q4_1:    ftype = LLAMA_FTYPE_MOSTLY_Q4_1;    break;
                case GGML_TYPE_Q5_0:    ftype = LLAMA_FTYPE_MOSTLY_Q5_0;    break;
                case GGML_TYPE_Q5_1:    ftype = LLAMA_FTYPE_MOSTLY_Q5_1;    break;
                case GGML_TYPE_Q8_0:    ftype = LLAMA_FTYPE_MOSTLY_Q8_0;    break;
                case GGML_TYPE_Q2_K:    ftype = LLAMA_FTYPE_MOSTLY_Q2_K;    break;
                case GGML_TYPE_Q3_K:    ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M;  break;
                case GGML_TYPE_Q4_K:    ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M;  break;
                case GGML_TYPE_Q5_K:    ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M;  break;
                case GGML_TYPE_Q6_K:    ftype = LLAMA_FTYPE_MOSTLY_Q6_K;    break;
                case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
                case GGML_TYPE_IQ2_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS;  break;
                case GGML_TYPE_IQ2_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ2_S;   break;
                case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
                case GGML_TYPE_IQ1_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_S;   break;
                case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
                case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
                case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
                default:
                    {
                        LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
                        ftype = LLAMA_FTYPE_ALL_F32;
                    } break;
            }

            // this is a way to mark that we have "guessed" the file type
            ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);

            {
                const int kid = gguf_find_key(ctx_gguf, "general.file_type");
                if (kid >= 0) {
                    ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
                }
            }

            LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
            for (int i = 0; i < n_kv; i++) {
                const char * name           = gguf_get_key(ctx_gguf, i);
                const enum gguf_type type   = gguf_get_kv_type(ctx_gguf, i);
                const std::string type_name =
                    type == GGUF_TYPE_ARRAY
                    ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx_gguf, i)), gguf_get_arr_n(ctx_gguf, i))
                    : gguf_type_name(type);

                std::string value          = gguf_kv_to_str(ctx_gguf, i);
                const size_t MAX_VALUE_LEN = 40;
                if (value.size() > MAX_VALUE_LEN) {
                    value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
                }
                replace_all(value, "\n", "\\n");

                LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
            }

            // print type counts
            for (auto & kv : n_type) {
                if (kv.second == 0) {
                    continue;
                }

                LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
            }
        }

        if (!llama_mmap::SUPPORTED) {
            LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
            use_mmap = false;
        }

        this->use_mmap = use_mmap;
    }

    ~llama_model_loader() {
        if (ctx_gguf) {
            gguf_free(ctx_gguf);
        }
        if (ctx_meta) {
            ggml_free(ctx_meta);
        }
    }

    template<typename T>
    typename std::enable_if<std::is_integral<T>::value, bool>::type
    get_arr_n(const std::string & key, T & result, const bool required = true) {
        const int kid = gguf_find_key(ctx_gguf, key.c_str());

        if (kid < 0) {
            if (required) {
                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
            }
            return false;
        }

        struct GGUFMeta::ArrayInfo arr_info =
            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx_gguf, kid);


        result = arr_info.length;
        return true;
    }

    template<typename T>
    typename std::enable_if<std::is_integral<T>::value, bool>::type
    get_arr_n(const enum llm_kv kid, T & result, const bool required = true) {
        return get_arr_n(llm_kv(kid), result, required);
    }

    template<typename T>
    bool get_key(const std::string & key, T & result, const bool required = true) {
        auto it = kv_overrides.find(key);

        const struct llama_model_kv_override * override =
            it != kv_overrides.end() ? &it->second : nullptr;

        const bool found = GGUFMeta::GKV<T>::set(ctx_gguf, key, result, override);

        if (required && !found) {
            throw std::runtime_error(format("key not found in model: %s", key.c_str()));
        }

        return found;
    }

    template<typename T>
    bool get_key(const enum llm_kv kid, T & result, const bool required = true) {
        return get_key(llm_kv(kid), result, required);
    }

    std::string get_arch_name() const {
        return arch_name;
    }

    enum llm_arch get_arch() const {
        return llm_kv.arch;
    }

    const char * get_tensor_name(int i) const {
        return gguf_get_tensor_name(ctx_gguf, i);
    }

    struct ggml_tensor * get_tensor_meta(const char * name) const {
        return ggml_get_tensor(ctx_meta, name);
    }

    struct ggml_tensor * get_tensor_meta(int i) const {
        return get_tensor_meta(get_tensor_name(i));
    }

    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta) {
        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
        ggml_set_name(tensor, ggml_get_name(meta));

        n_created++;

        return tensor;
    }

    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
        struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());

        if (cur == NULL) {
            if (!required) {
                return NULL;
            }
            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
        }

        {
            bool is_ok = true;
            for (size_t i = 0; i < ne.size(); ++i) {
                if (ne[i] != cur->ne[i]) {
                    is_ok = false;
                    break;
                }
            }
            if (!is_ok) {
                throw std::runtime_error(
                        format("%s: tensor '%s' has wrong shape; expected %s, got %s",
                            __func__, name.c_str(),
                            llama_format_tensor_shape(ne).c_str(),
                            llama_format_tensor_shape(cur).c_str()));
            }
        }

        return create_tensor_for(ctx, cur);
    }

    void done_getting_tensors() const {
        if (n_created != n_tensors) {
            throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
        }
    }

    size_t file_offset(const char * name) const {
        const int idx = gguf_find_tensor(ctx_gguf, name);

        if (idx < 0) {
            throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
        }

        return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
    }

    void init_mapping(bool prefetch = true, llama_mlock * lmlock = nullptr) {
        // prefetch the whole file - all the data is needed anyway
        if (use_mmap) {
            mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa()));
        }

        // compute the total size of all tensors for progress reporting
        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
            size_data += ggml_nbytes(cur);
        }

        if (use_mmap && mapping) {
            if (lmlock) {
                lmlock->init(mapping->addr);
            }
            mmap_used_first = mapping->size;
        }
    }

    void get_mapping_range(size_t * first, size_t * last, ggml_context * ctx) const {
        GGML_ASSERT(mapping);

        *first = mapping->size;
        *last  = 0;
        for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
            const size_t offs = file_offset(ggml_get_name(tensor));
            *first = std::min(*first, offs);
            *last  = std::max(*last,  offs + ggml_nbytes(tensor));
        }
    }

    // for backwards compatibility, does not support ggml-backend
    void load_data_for(struct ggml_tensor * cur) const {
        const size_t offs = file_offset(ggml_get_name(cur));

        if (use_mmap && mapping) {
            if (cur->data == nullptr) {
                cur->data = (uint8_t *)mapping->addr + offs;
            } else {
                memcpy(cur->data, (uint8_t *)mapping->addr + offs, ggml_nbytes(cur));
            }
        } else {
            GGML_ASSERT(cur->data != nullptr);
            file.seek(offs, SEEK_SET);
            file.read_raw(cur->data, ggml_nbytes(cur));
        }
    }

    size_t size_done = 0;
    size_t size_data = 0;
    size_t mmap_used_first = -1;
    size_t mmap_used_last  = 0;

    // Returns false if cancelled by progress_callback
    bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) {
        GGML_ASSERT(size_data != 0 && "call init_mapping() first");

        std::vector<no_init<uint8_t>> read_buf;

        for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
            if (progress_callback) {
                if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
                    return false;
                }
            }

            const size_t offs = file_offset(ggml_get_name(cur));

            if (use_mmap && mapping) {
                if (buf_mmap && cur->data == nullptr) {
                    ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs);
                    if (lmlock) {
                        lmlock->grow_to(offs + ggml_nbytes(cur));
                    }
                    mmap_used_first = std::min(mmap_used_first, offs);
                    mmap_used_last  = std::max(mmap_used_last,  offs + ggml_nbytes(cur));
                } else {
                    ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur));
                }
            } else {
                if (ggml_backend_buffer_is_host(cur->buffer)) {
                    file.seek(offs, SEEK_SET);
                    file.read_raw(cur->data, ggml_nbytes(cur));
                } else {
                    read_buf.resize(ggml_nbytes(cur));
                    file.seek(offs, SEEK_SET);
                    file.read_raw(read_buf.data(), ggml_nbytes(cur));
                    ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur));
                }
            }

            size_done += ggml_nbytes(cur);
        }

        // check if this is the last call and do final cleanup
        if (size_done >= size_data) {
            // unmap offloaded tensors and metadata
            if (use_mmap && mapping) {
                mapping->unmap_fragment(0, mmap_used_first);
                if (mmap_used_last != 0) {
                    mapping->unmap_fragment(mmap_used_last, mapping->size);
                }
            }
            if (progress_callback) {
                // Even though the model is done loading, we still honor
                // cancellation since we need to free allocations.
                return progress_callback(1.0f, progress_callback_user_data);
            }
        }

        return true;
    }
};

template<>
bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
    uint32_t tmp;
    const bool found = get_key(kid, tmp, required);
    if (found) {
        result = (enum llama_pooling_type) tmp;
    } else {
        result = LLAMA_POOLING_TYPE_UNSPECIFIED;
    }
    return found;
}


//
// load LLaMA models
//

static const char * llama_model_arch_name(llm_arch arch) {
    auto it = LLM_ARCH_NAMES.find(arch);
    if (it == LLM_ARCH_NAMES.end()) {
        return "unknown";
    }
    return it->second;
}

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_Q4_0: return "Q4_0";
        case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
        case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
                                      return "Q4_1, some F16";
        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";

        // K-quants
        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_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_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";
    }
}

static const char * llama_model_type_name(e_model type) {
    switch (type) {
        case MODEL_22M:    return "22M";
        case MODEL_33M:    return "33M";
        case MODEL_109M:   return "109M";
        case MODEL_137M:   return "137M";
        case MODEL_0_5B:   return "0.5B";
        case MODEL_1B:     return "1B";
        case MODEL_2B:     return "2B";
        case MODEL_3B:     return "3B";
        case MODEL_7B:     return "7B";
        case MODEL_8B:     return "8B";
        case MODEL_13B:    return "13B";
        case MODEL_14B:    return "14B";
        case MODEL_15B:    return "15B";
        case MODEL_20B:    return "20B";
        case MODEL_30B:    return "30B";
        case MODEL_34B:    return "34B";
        case MODEL_40B:    return "40B";
        case MODEL_65B:    return "65B";
        case MODEL_70B:    return "70B";
        case MODEL_SMALL:  return "0.1B";
        case MODEL_MEDIUM: return "0.4B";
        case MODEL_LARGE:  return "0.8B";
        case MODEL_XL:     return "1.5B";
        default:           return "?B";
    }
}

static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
    switch (type) {
        case LLAMA_VOCAB_TYPE_SPM: return "SPM";
        case LLAMA_VOCAB_TYPE_BPE: return "BPE";
        case LLAMA_VOCAB_TYPE_WPM: return "WPM";
        default:                   return "unknown";
    }
}

static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
    model.arch = ml.get_arch();
    if (model.arch == LLM_ARCH_UNKNOWN) {
        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
    }
}

static void llm_load_hparams(
        llama_model_loader & ml,
        llama_model & model) {
    auto & hparams = model.hparams;
    const gguf_context * ctx = ml.ctx_gguf;

    // get metadata as string
    for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
        enum gguf_type type = gguf_get_kv_type(ctx, i);
        if (type == GGUF_TYPE_ARRAY) {
            continue;
        }
        const char * name = gguf_get_key(ctx, i);
        const std::string value = gguf_kv_to_str(ctx, i);
        model.gguf_kv.emplace(name, value);
    }

    // get general kv
    ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);

    // get hparams kv
    ml.get_arr_n(LLM_KV_TOKENIZER_LIST,       hparams.n_vocab);
    ml.get_key  (LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
    ml.get_key  (LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
    ml.get_key  (LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
    ml.get_key  (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
    ml.get_key  (LLM_KV_BLOCK_COUNT,          hparams.n_layer);
    ml.get_key  (LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
    ml.get_key  (LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);

    GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
    GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
    if (hparams.n_expert > 0) {
        GGML_ASSERT(hparams.n_expert_used > 0);
    } else {
        GGML_ASSERT(hparams.n_expert_used == 0);
    }

    // n_head_kv is optional, default to n_head
    hparams.n_head_kv = hparams.n_head;
    ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv, false);

    bool rope_finetuned = false;
    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
    hparams.rope_finetuned = rope_finetuned;

    hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_yarn_orig_ctx, false);

    // rope_freq_base (optional)
    hparams.rope_freq_base_train = 10000.0f;
    ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);

    std::string rope_scaling("linear");
    ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
    hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);

    // rope_freq_scale (inverse of the kv) is optional
    float ropescale = 0.0f;
    if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
        // try the old key name
        ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
    }
    hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;

    // sanity check for n_rot (optional)
    {
        hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;

        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);

        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
            if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
            }
        }
        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
        // gpt-j n_rot = rotary_dim
    }

    hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);

    hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);

    // arch-specific KVs
    switch (model.arch) {
        case LLM_ARCH_LLAMA:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

                switch (hparams.n_layer) {
                    case 22: model.type = e_model::MODEL_1B; break;
                    case 26: model.type = e_model::MODEL_3B; break;
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_13B; break;
                    case 48: model.type = e_model::MODEL_34B; break;
                    case 60: model.type = e_model::MODEL_30B; break;
                    case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_MINICPM:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

                switch (hparams.n_layer) {
                    case 40: model.type = e_model::MODEL_2B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_FALCON:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 60: model.type = e_model::MODEL_40B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_BAICHUAN:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_13B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }

                if (model.type == e_model::MODEL_13B) {
                    // TODO: become GGUF KV parameter
                    hparams.f_max_alibi_bias = 8.0f;
                }
            } break;
        case LLM_ARCH_STARCODER:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_1B; break;
                    case 36: model.type = e_model::MODEL_3B; break;
                    case 42: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_15B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_PERSIMMON:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                switch (hparams.n_layer) {
                    case 36: model.type = e_model::MODEL_8B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_REFACT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_1B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }

                // TODO: become GGUF KV parameter
                hparams.f_max_alibi_bias = 8.0f;
            } break;
        case LLM_ARCH_BERT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);

                switch (hparams.n_layer) {
                    case 3:
                        model.type = e_model::MODEL_17M; break; // bge-micro
                    case 6:
                        model.type = e_model::MODEL_22M; break; // MiniLM-L6
                    case 12:
                        switch (hparams.n_embd) {
                            case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
                            case 768: model.type = e_model::MODEL_109M; break; // bge-base
                        } break;
                    case 24:
                        model.type = e_model::MODEL_335M; break; // bge-large
                }
            } break;
        case LLM_ARCH_NOMIC_BERT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);

                if (hparams.n_layer == 12 && hparams.n_embd == 768) {
                    model.type = e_model::MODEL_137M;
                }
            } break;
        case LLM_ARCH_BLOOM:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_1B; break;
                    case 30:
                        switch (hparams.n_embd) {
                            case 2560: model.type = e_model::MODEL_3B; break;
                            case 4096: model.type = e_model::MODEL_7B; break;
                        } break;
                }

                // TODO: become GGUF KV parameter
                hparams.f_max_alibi_bias = 8.0f;
            } break;
        case LLM_ARCH_MPT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
                ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);
                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);

                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 48: model.type = e_model::MODEL_30B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_STABLELM:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_1B; break;
                    case 32: model.type = e_model::MODEL_3B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
               }
            } break;
        case LLM_ARCH_QWEN:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_13B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_QWEN2:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
                    case 80: model.type = e_model::MODEL_70B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_PHI2:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_1B; break;
                    case 32: model.type = e_model::MODEL_3B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_PLAMO:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

                switch (hparams.n_layer) {
                    case 40: model.type = e_model::MODEL_13B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
               }
            } break;
        case LLM_ARCH_GPT2:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                switch (hparams.n_layer) {
                    case 12: model.type = e_model::MODEL_SMALL; break;
                    case 24: model.type = e_model::MODEL_MEDIUM; break;
                    case 36: model.type = e_model::MODEL_LARGE; break;
                    case 48: model.type = e_model::MODEL_XL; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_CODESHELL:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                switch (hparams.n_layer) {
                    case 42: model.type = e_model::MODEL_SMALL; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_ORION:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

                switch (hparams.n_layer) {
                    case 40: model.type = e_model::MODEL_14B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_INTERNLM2:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 48: model.type = e_model::MODEL_20B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_GEMMA:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

                switch (hparams.n_layer) {
                    case 18: model.type = e_model::MODEL_2B; break;
                    case 28: model.type = e_model::MODEL_7B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
               }
            } break;
        case LLM_ARCH_STARCODER2:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                switch (hparams.n_layer) {
                    case 30: model.type = e_model::MODEL_3B; break;
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_15B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_MAMBA:
            {
                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);

                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

                switch (hparams.n_layer) {
                    case 24:
                        switch (hparams.n_embd) {
                            case 768: model.type = e_model::MODEL_SMALL; break;
                            default: model.type = e_model::MODEL_UNKNOWN;
                        } break;
                    case 48:
                        switch (hparams.n_embd) {
                            case 1024: model.type = e_model::MODEL_MEDIUM; break;
                            case 1536: model.type = e_model::MODEL_LARGE; break;
                            case 2048: model.type = e_model::MODEL_XL; break;
                            default: model.type = e_model::MODEL_UNKNOWN;
                        } break;
                    case 64:
                        switch (hparams.n_embd) {
                            case 2560: model.type = e_model::MODEL_3B; break;
                            default: model.type = e_model::MODEL_UNKNOWN;
                        } break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        default: (void)0;
    }

    model.ftype = ml.ftype;

    if (hparams.f_max_alibi_bias > 0.0f) {
        hparams.need_kq_pos = true;
    }

    hparams.rope_type = llama_rope_type(&model);
}

// TODO: This should probably be in llama.h
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false);
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);

static void llm_load_vocab(
        llama_model_loader & ml,
        llama_model & model) {
    auto & vocab = model.vocab;

    struct gguf_context * ctx = ml.ctx_gguf;

    const auto kv = LLM_KV(model.arch);

    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
    if (token_idx == -1) {
        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
    }

    const float * scores = nullptr;
    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
    if (score_idx != -1) {
        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
    }

    const int * toktypes = nullptr;
    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
    if (toktype_idx != -1) {
        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
    }

    // determine vocab type
    {
        std::string tokenizer_name;

        ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);

        if (tokenizer_name == "llama") {
            vocab.type = LLAMA_VOCAB_TYPE_SPM;

            // default special tokens
            vocab.special_bos_id = 1;
            vocab.special_eos_id = 2;
            vocab.special_unk_id = 0;
            vocab.special_sep_id = -1;
            vocab.special_pad_id = -1;

            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
            if (add_space_prefix_keyidx != -1) {
                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
            } // The default value of add_space_prefix is true.
        } else if (tokenizer_name == "gpt2") {
            vocab.type = LLAMA_VOCAB_TYPE_BPE;

            // read bpe merges and populate bpe ranks
            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
            if (merges_keyidx == -1) {
                throw std::runtime_error("cannot find tokenizer merges in model file\n");
            }

            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);

            for (int i = 0; i < n_merges; i++) {
                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
                GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);

                std::string first;
                std::string second;

                const size_t pos = word.find(' ', 1);

                if (pos != std::string::npos) {
                    first  = word.substr(0, pos);
                    second = word.substr(pos + 1);
                }

                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
            }

            // default special tokens
            vocab.special_bos_id = 11;
            vocab.special_eos_id = 11;
            vocab.special_unk_id = -1;
            vocab.special_sep_id = -1;
            vocab.special_pad_id = -1;
        } else if (tokenizer_name == "bert") {
            vocab.type = LLAMA_VOCAB_TYPE_WPM;

            // default special tokens
            vocab.special_bos_id = 101;
            vocab.special_eos_id = 102;
            vocab.special_unk_id = 100;
            vocab.special_sep_id = -1;
            vocab.special_pad_id = -1;
            vocab.add_space_prefix = false;
        } else {
            LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
            LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);

            vocab.type = LLAMA_VOCAB_TYPE_SPM;
        }
    }

    const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);

    vocab.id_to_token.resize(n_vocab);

    for (uint32_t i = 0; i < n_vocab; i++) {
        std::string word = gguf_get_arr_str(ctx, token_idx, i);
        GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);

        vocab.token_to_id[word] = i;

        auto & token_data = vocab.id_to_token[i];
        token_data.text  = std::move(word);
        token_data.score = scores ? scores[i] : 0.0f;
        token_data.type  = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
    }
    GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());

    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
        try {
            vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
        } catch (const std::exception & e) {
            LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
            vocab.linefeed_id = vocab.special_pad_id;
        }
    } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
        vocab.linefeed_id = vocab.special_pad_id;
    } else {
        const std::vector<int> ids = llama_tokenize_internal(vocab, "\u010A", false);
        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
        vocab.linefeed_id = ids[0];
    }

    // special tokens
    {
        const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
            { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
            { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
            { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
            { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
            { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
        };
        for (const auto & it : special_token_types) {
            const std::string & key = kv(std::get<0>(it));
            int32_t & id = std::get<1>(it);

            uint32_t new_id;
            if (!ml.get_key(std::get<0>(it), new_id, false)) {
                continue;
            }
            if (new_id >= vocab.id_to_token.size()) {
                LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
                    __func__, key.c_str(), new_id, id);
            } else {
                id = new_id;
            }

        }

        // Handle add_bos_token and add_eos_token
        {
            bool temp = true;

            if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
                vocab.special_add_bos = int(temp);
            }
            if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
                vocab.special_add_eos = int(temp);
            }
        }
    }

    // build special tokens cache
    {
        // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
        //  and will always be correctly labeled in 'added_tokens.json' etc.
        // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
        //  to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
        //  are special tokens.
        // From testing, this appears to correlate 1:1 with special tokens.
        //

        // Counting special tokens and verifying in only one direction
        //  is sufficient to detect difference in those two sets.
        //
        uint32_t special_tokens_count_by_type = 0;
        uint32_t special_tokens_count_from_verification = 0;

        bool special_tokens_definition_mismatch = false;

        for (const auto & t : vocab.token_to_id) {
            const auto & token = t.first;
            const auto & id    = t.second;

            // Count all non-normal tokens in the vocab while iterating
            if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
                special_tokens_count_by_type++;
            }

            // Skip single character tokens
            if (token.length() > 1) {
                bool is_tokenizable = false;

                // Split token string representation in two, in all possible ways
                //  and check if both halves can be matched to a valid token
                for (unsigned i = 1; i < token.length();) {
                    const auto left  = token.substr(0, i);
                    const auto right = token.substr(i);

                    // check if we didnt partition in the middle of a utf sequence
                    auto utf = utf8_len(left.at(left.length() - 1));

                    if (utf == 1) {
                        if (vocab.token_to_id.find(left)  != vocab.token_to_id.end() &&
                            vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
                            is_tokenizable = true;
                            break;
                        }
                        i++;
                    } else {
                        // skip over the rest of multibyte utf sequence
                        i += utf - 1;
                    }
                }

                if (!is_tokenizable) {
                    // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
                    //  it's faster to re-filter them here, since there are way less candidates now

                    // Calculate a total "utf" length of a token string representation
                    size_t utf8_str_len = 0;
                    for (unsigned i = 0; i < token.length();) {
                        utf8_str_len++;
                        i += utf8_len(token.at(i));
                    }

                    // And skip the ones which are one character
                    if (utf8_str_len > 1) {
                        // At this point what we have left are special tokens only
                        vocab.special_tokens_cache[token] = id;

                        // Count manually found special tokens
                        special_tokens_count_from_verification++;

                        // If this manually found special token is not marked as such, flag a mismatch
                        if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
                            special_tokens_definition_mismatch = true;
                        }
                    }
                }
            }
        }

        if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
            LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
                __func__,
                special_tokens_count_from_verification, vocab.id_to_token.size(),
                special_tokens_count_by_type, vocab.id_to_token.size()
            );
        } else {
            LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
                __func__,
                special_tokens_count_from_verification, vocab.id_to_token.size()
            );
        }
    }
}

static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
    const auto & hparams = model.hparams;
    const auto & vocab   = model.vocab;

    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);

    // hparams
    LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
    LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
    LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
    LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
    LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
    LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
    LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
    LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
    LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
    LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %u\n",     __func__, hparams.n_embd_k_gqa());
    LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %u\n",     __func__, hparams.n_embd_v_gqa());
    LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
    LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
    LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
    LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
    LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
    LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
    LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
    LLAMA_LOG_INFO("%s: causal attm      = %d\n",     __func__, hparams.causal_attn);
    LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
    LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
    LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
    LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
    LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
    LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
    LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
    LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
    LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
    LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
    LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
    if (ml.n_elements >= 1e12) {
        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, ml.n_elements*1e-12);
    } else if (ml.n_elements >= 1e9) {
        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
    } else if (ml.n_elements >= 1e6) {
        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, ml.n_elements*1e-6);
    } else {
        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, ml.n_elements*1e-3);
    }
    if (ml.n_bytes < GiB) {
        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
    } else {
        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
    }

    // general kv
    LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());

    // special tokens
    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
}

// Returns false if cancelled by progress_callback
static bool llm_load_tensors(
        llama_model_loader & ml,
        llama_model & model,
        int n_gpu_layers,
        enum llama_split_mode split_mode,
        int main_gpu,
        const float * tensor_split,
        bool use_mlock,
        llama_progress_callback progress_callback,
        void * progress_callback_user_data) {
    model.t_start_us = ggml_time_us();

    auto & hparams = model.hparams;

    model.split_mode   = split_mode;
    model.main_gpu     = main_gpu;
    model.n_gpu_layers = n_gpu_layers;

    const int64_t n_layer     = hparams.n_layer;
    const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);

    // there is very little benefit to offloading the input layer, so always keep it on the CPU
    model.buft_input = llama_default_buffer_type_cpu(true);
    //model.buft_input = llama_default_buffer_type_offload(main_gpu);

    model.buft_layer.resize(n_layer);

    // assign cpu layers
    for (int64_t i = 0; i < i_gpu_start; ++i) {
        model.buft_layer[i] = llama_default_buffer_type_cpu(true);
    }

    if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
        // calculate the split points
        int device_count = llama_get_device_count();
        bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
        std::vector<float> splits(device_count);
        if (all_zero) {
            // default split, by free memory
            for (int i = 0; i < device_count; ++i) {
                splits[i] = llama_get_device_memory(i);
            }
        } else {
            std::copy(tensor_split, tensor_split + device_count, splits.begin());
        }

        // sum and normalize the splits to get the split points
        float split_sum = 0.0f;
        for (int i = 0; i < device_count; ++i) {
            split_sum += splits[i];
            splits[i] = split_sum;
        }
        for (int i = 0; i < device_count; ++i) {
            splits[i] /= split_sum;
        }

        // assign the repeating layers to the devices according to the splits
        int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
        for (int64_t i = i_gpu_start; i < n_layer; ++i) {
            int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
            model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
        }
        // assign the output layer
        if (n_gpu_layers > n_layer) {
            int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
            model.buft_output = llama_default_buffer_type_offload(layer_gpu);
        } else {
            model.buft_output = llama_default_buffer_type_cpu(true);
        }
    } else {
        ggml_backend_buffer_type_t split_buft;
        if (split_mode == LLAMA_SPLIT_MODE_ROW) {
            split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
        } else {
            // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
            split_buft = llama_default_buffer_type_offload(main_gpu);
        }
        // assign the repeating layers
        for (int64_t i = i_gpu_start; i < n_layer; ++i) {
            model.buft_layer[i] = {
                split_buft,
                llama_default_buffer_type_offload(main_gpu)
            };
        }
        // assign the output layer
        if (n_gpu_layers > n_layer) {
            model.buft_output = {
                split_buft,
                llama_default_buffer_type_offload(main_gpu)
            };
        } else {
            model.buft_output = llama_default_buffer_type_cpu(true);
        }
    }

    // count used buffer types
    std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
    buft_layer_count[model.buft_input.buft]++;
    buft_layer_count[model.buft_input.buft_matrix]++;
    buft_layer_count[model.buft_output.buft]++;
    buft_layer_count[model.buft_output.buft_matrix]++;
    for (int64_t i = 0; i < n_layer; ++i) {
        buft_layer_count[model.buft_layer[i].buft]++;
        buft_layer_count[model.buft_layer[i].buft_matrix]++;
    }

    // create one context per buffer type
    size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
    for (auto & it : buft_layer_count) {
        struct ggml_init_params params = {
            /*.mem_size   =*/ ctx_size,
            /*.mem_buffer =*/ NULL,
            /*.no_alloc   =*/ true,
        };
        ggml_context * ctx = ggml_init(params);
        if (!ctx) {
            throw std::runtime_error(format("failed to create context"));
        }
        ctx_map[it.first] = ctx;
        model.ctxs.push_back(ctx);
    }

    LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0);

    // create tensors for the weights
    {
        const int64_t n_embd       = hparams.n_embd;
        const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
        const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
        const int64_t n_embd_gqa   = n_embd_v_gqa;
        const int64_t n_vocab      = hparams.n_vocab;
        const int64_t n_vocab_type = hparams.n_vocab_type;
        const int64_t n_ff         = hparams.n_ff;

        GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);

        ggml_context * ctx_input        = ctx_map.at(model.buft_input.buft);
        ggml_context * ctx_output       = ctx_map.at(model.buft_output.buft);
        ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
        auto ctx_for_layer              = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
        auto ctx_for_layer_split        = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };

        model.layers.resize(n_layer);

        const auto tn = LLM_TN(model.arch);
        switch (model.arch) {
            case LLM_ARCH_LLAMA:
            case LLM_ARCH_REFACT:
            case LLM_ARCH_MINICPM:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        if (model.arch != LLM_ARCH_MINICPM){
                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
                            // if output is NULL, init from the input tok embed
                            if (model.output == NULL) {
                                model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                                ml.n_created--; // artificial tensor
                                ml.size_data += ggml_nbytes(model.output);
                            }
                        }
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        // optional bias tensors
                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     false);

                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});

                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, false);

                        if (layer.ffn_gate_inp == nullptr) {
                            GGML_ASSERT(hparams.n_expert      == 0);
                            GGML_ASSERT(hparams.n_expert_used == 0);

                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                            layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                            layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                        } else {
                            GGML_ASSERT(hparams.n_expert      > 0);
                            GGML_ASSERT(hparams.n_expert_used > 0);

                            // MoE branch
                            for (uint32_t x = 0; x < hparams.n_expert; ++x) {
                                layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd,   n_ff});
                                layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), {  n_ff, n_embd});
                                layer.ffn_up_exp[x]   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), {n_embd,   n_ff});
                            }
                        }
                    }
                } break;
            case LLM_ARCH_BAICHUAN:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                    {
                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});

                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_FALCON:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_OUTPUT, "weight").c_str()) >= 0) {
                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,     "weight"), {n_embd, n_vocab});
                        } else {
                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
                            ml.n_created--; // artificial tensor
                            ml.size_data += ggml_nbytes(model.output);
                        }
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
                            layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd});
                            layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd});
                        }

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_STARCODER:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});

                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});

                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
                    }
                } break;
            case LLM_ARCH_PERSIMMON:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab});

                    {
                        model.output_norm    = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b  = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output         = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
                        layer.attn_norm_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd});

                        layer.wqkv          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.bqkv          = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa});

                        layer.wo            = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd});
                        layer.bo            = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd});

                        layer.ffn_down      = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd});

                        layer.ffn_up        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd, n_ff});
                        layer.ffn_up_b      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff});

                        layer.ffn_norm      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd});
                        layer.ffn_norm_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd});

                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64});

                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64});
                    }
                } break;
            case LLM_ARCH_BERT:
            case LLM_ARCH_NOMIC_BERT:
                {
                    model.tok_embd     = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab});
                    model.type_embd    = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
                    if (model.arch == LLM_ARCH_BERT) {
                        model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, hparams.n_ctx_train});
                    }

                    model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
                    model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd});

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        if (model.arch == LLM_ARCH_BERT) {
                            layer.wq   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                            layer.bq   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});

                            layer.wk   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                            layer.bk   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});

                            layer.wv   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                            layer.bv   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
                        } else {
                            layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        }

                        layer.wo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd});

                        layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
                        layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});

                        layer.ffn_up          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
                        layer.ffn_down        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd});

                        if (model.arch == LLM_ARCH_BERT) {
                            layer.bo         = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
                            layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});

                            layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
                        } else {
                            layer.ffn_gate   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        }

                        layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
                        layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd});
                    }
                } break;
            case LLM_ARCH_BLOOM:
                {
                    model.tok_embd   = ml.create_tensor(ctx_input,  tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
                    model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
                    model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});

                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});

                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                    }
                } break;
            case LLM_ARCH_MPT:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, false);

                        // same as tok_embd, duplicated to allow offloading
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab});
                        ml.n_created--; // artificial tensor
                        ml.size_data += ggml_nbytes(model.output);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, false);

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);

                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, false);

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);

                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, false);

                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, false);

                        // AWQ ScaleActivation layer
                        layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
                    }
                } break;
            case LLM_ARCH_STABLELM:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm =   ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        // optional bias tensors, present in Stable LM 2 1.6B
                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_QWEN:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3});
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd*3});
                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});

                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2});
                    }
                } break;
            case LLM_ARCH_QWEN2:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        // optional bias tensors
                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd});
                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa});
                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa});

                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});

                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_PHI2:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                        model.output_b      = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);

                        if (layer.wqkv == nullptr) {
                            layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
                            layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd});

                            layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
                            layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i),   {n_embd_gqa});

                            layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
                            layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i),   {n_embd_gqa});
                        }

                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});

                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});

                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                    }
                } break;
            case LLM_ARCH_PLAMO:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_GPT2:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"),   {n_embd, hparams.n_ctx_train});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd});

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});

                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});

                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                    }
                } break;
            case LLM_ARCH_CODESHELL:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});

                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});

                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
                    }
                } break;
            case LLM_ARCH_ORION:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                    {
                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }
                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_INTERNLM2:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});

                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_GEMMA:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
                    ml.n_created--; // artificial tensor
                    ml.size_data += ggml_nbytes(model.output);

                    const int64_t n_ff          = hparams.n_ff;
                    const int64_t n_embd_head_k = hparams.n_embd_head_k;
                    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
                    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});

                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                    }
                } break;
            case LLM_ARCH_STARCODER2:
                {
                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});

                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
                        // if output is NULL, init from the input tok embed
                        if (model.output == NULL) {
                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                            ml.n_created--; // artificial tensor
                            ml.size_data += ggml_nbytes(model.output);
                        }

                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});

                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

                        // optional bias tensors
                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd});
                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa});
                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa});
                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});

                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});

                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});

                        // optional bias tensors
                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff});
                    }
                } break;
            case LLM_ARCH_MAMBA:
                {
                    const int64_t d_conv  = hparams.ssm_d_conv;
                    const int64_t d_inner = hparams.ssm_d_inner;
                    const int64_t d_state = hparams.ssm_d_state;
                    const int64_t dt_rank = hparams.ssm_dt_rank;
                    // only an expansion factor of 2 is supported for now
                    GGML_ASSERT(2 * n_embd == d_inner);

                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});

                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
                        if (model.output == NULL) {
                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
                            ml.n_created--; // artificial tensor
                            ml.size_data += ggml_nbytes(model.output);
                        }
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        ggml_context * ctx_layer = ctx_for_layer(i);
                        ggml_context * ctx_split = ctx_for_layer_split(i);

                        auto & layer = model.layers[i];

                        // norm
                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});

                        layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});

                        layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
                        layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});

                        layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});

                        layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
                        layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});

                        // no "weight" suffix for these
                        layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
                        layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});

                        // out_proj
                        layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
                    }
                } break;
            default:
                throw std::runtime_error("unknown architecture");
        }
    }

    ml.done_getting_tensors();

    ml.init_mapping(true, use_mlock ? &model.mlock_mmap : nullptr);

    // create the backend buffers
    std::vector<std::pair<ggml_context *, ggml_backend_buffer_t>> ctx_bufs;

    for (auto & it : ctx_map) {
        ggml_backend_buffer_type_t buft = it.first;
        ggml_context * ctx = it.second;
        ggml_backend_buffer_t buf = nullptr;

        // only the mmap region containing the tensors in the model is mapped to the backend buffer
        // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
        // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
        if (ml.use_mmap && buft == llama_default_buffer_type_cpu(true)) {
            size_t first, last;
            ml.get_mapping_range(&first, &last, ctx);
            buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first);
        }
#ifdef GGML_USE_METAL
        else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) {
            const size_t max_size = ggml_get_max_tensor_size(ctx);
            size_t first, last;
            ml.get_mapping_range(&first, &last, ctx);
            buf = ggml_backend_metal_buffer_from_ptr((char *) ml.mapping->addr + first, last - first, max_size);
        }
#endif
        else {
            buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
            if (buf != nullptr && use_mlock && ggml_backend_buffer_is_host(buf)) {
                model.mlock_bufs.emplace_back(new llama_mlock);
                auto & mlock_buf = model.mlock_bufs.back();
                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
            }
        }
        if (buf == nullptr) {
            throw std::runtime_error("failed to allocate buffer");
        }
        // indicate that this buffer contains weights
        // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
        ggml_backend_buffer_set_usage(buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
        model.bufs.push_back(buf);
        ctx_bufs.emplace_back(ctx, buf);
    }

    if (llama_supports_gpu_offload()) {
        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));

        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
        if (n_gpu_layers > (int) hparams.n_layer) {
            LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
        }

        const int max_backend_supported_layers = hparams.n_layer + 1;
        const int max_offloadable_layers       = hparams.n_layer + 1;

        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
    }

    // print memory requirements
    for (ggml_backend_buffer_t buf : model.bufs) {
        LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
    }

    // populate tensors_by_name
    for (ggml_context * ctx : model.ctxs) {
        for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
            model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
        }
    }

    // load tensor data
    for (auto & it : ctx_bufs) {
        ggml_context * ctx = it.first;
        ggml_backend_buffer_t buf = it.second;
        if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf, use_mlock ? &model.mlock_mmap : NULL)) {
            return false;
        }
    }

    model.mapping = std::move(ml.mapping);

    // loading time will be recalculate after the first eval, so
    // we take page faults deferred by mmap() into consideration
    model.t_load_us = ggml_time_us() - model.t_start_us;
    return true;
}

// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
    try {
        llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);

        model.hparams.vocab_only = params.vocab_only;

        try {
            llm_load_arch(ml, model);
        } catch(const std::exception & e) {
            throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
        }
        try {
            llm_load_hparams(ml, model);
        } catch(const std::exception & e) {
            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
        }
        try {
            llm_load_vocab(ml, model);
        } catch(const std::exception & e) {
            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
        }

        llm_load_print_meta(ml, model);

        if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
            throw std::runtime_error("vocab size mismatch");
        }

        if (params.vocab_only) {
            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
            return 0;
        }

#ifdef GGML_USE_KOMPUTE
        if (params.n_gpu_layers > 0 && (
            !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
            || !(
                model.ftype == LLAMA_FTYPE_ALL_F32 ||
                model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
            )
        )) {
            // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
            LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
            params.n_gpu_layers = 0;
        }
#endif

        if (!llm_load_tensors(
            ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
            params.progress_callback, params.progress_callback_user_data
        )) {
            return -2;
        }
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
        return -1;
    }

    return 0;
}

//
// llm_build
//

using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;

enum llm_ffn_op_type {
    LLM_FFN_SILU,
    LLM_FFN_GELU,
    LLM_FFN_RELU,
    LLM_FFN_RELU_SQR,
};

enum llm_ffn_gate_type {
    LLM_FFN_SEQ,
    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
};

enum llm_norm_type {
    LLM_NORM,
    LLM_NORM_RMS,
};

static struct ggml_tensor * llm_build_inp_embd(
        struct ggml_context * ctx,
       struct llama_context & lctx,
        const llama_hparams & hparams,
          const llama_batch & batch,
         struct ggml_tensor * tok_embd,
         const llm_build_cb & cb) {
    const int64_t n_embd = hparams.n_embd;

    struct ggml_tensor * inpL;

    if (batch.token) {
        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
        cb(lctx.inp_tokens, "inp_tokens", -1);
        ggml_set_input(lctx.inp_tokens);

        inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
    } else {
#ifdef GGML_USE_MPI
        GGML_ASSERT(false && "not implemented");
#endif
        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
        inpL = lctx.inp_embd;
        ggml_set_input(lctx.inp_embd);
    }

    cb(inpL, "inp_embd", -1);

    return inpL;
}

static void llm_build_kv_store(
        struct ggml_context * ctx,
        const llama_hparams & hparams,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * k_cur,
         struct ggml_tensor * v_cur,
                    int64_t   n_ctx,
                    int32_t   n_tokens,
                    int32_t   kv_head,
         const llm_build_cb & cb,
                    int64_t   il) {
    const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
    const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();

    GGML_ASSERT(kv.size == n_ctx);

    // compute the transposed [n_tokens, n_embd] V matrix
    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens));
    //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
    cb(v_cur_t, "v_cur_t", il);

    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
            (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
    cb(k_cache_view, "k_cache_view", il);

    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
            (  n_ctx)*ggml_element_size(kv.v_l[il]),
            (kv_head)*ggml_element_size(kv.v_l[il]));
    cb(v_cache_view, "v_cache_view", il);

    // important: storing RoPE-ed version of K in the KV cache!
    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur,   k_cache_view));
    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
}

static struct ggml_tensor * llm_build_norm(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
        const llama_hparams & hparams,
         struct ggml_tensor * mw,
         struct ggml_tensor * mb,
              llm_norm_type   type,
         const llm_build_cb & cb,
                        int   il) {
    switch (type) {
        case LLM_NORM:     cur = ggml_norm    (ctx, cur, hparams.f_norm_eps);     break;
        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
    }

    if (mw || mb) {
        cb(cur, "norm", il);
    }

    if (mw) {
        cur = ggml_mul(ctx, cur, mw);
        if (mb) {
            cb(cur, "norm_w", il);
        }
    }

    if (mb) {
        cur = ggml_add(ctx, cur, mb);
    }

    return cur;
}

static struct ggml_tensor * llm_build_ffn(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
         struct ggml_tensor * up,
         struct ggml_tensor * up_b,
         struct ggml_tensor * gate,
         struct ggml_tensor * gate_b,
         struct ggml_tensor * down,
         struct ggml_tensor * down_b,
         struct ggml_tensor * act_scales,
            llm_ffn_op_type   type_op,
          llm_ffn_gate_type   type_gate,
         const llm_build_cb & cb,
                        int   il) {
    struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
    cb(tmp, "ffn_up", il);

    if (up_b) {
        tmp = ggml_add(ctx, tmp, up_b);
        cb(tmp, "ffn_up_b", il);
    }

    if (gate) {
        switch (type_gate) {
            case LLM_FFN_SEQ:
                {
                    cur = ggml_mul_mat(ctx, gate, tmp);
                    cb(cur, "ffn_gate", il);
                } break;
            case LLM_FFN_PAR:
                {
                    cur = ggml_mul_mat(ctx, gate, cur);
                    cb(cur, "ffn_gate", il);
                } break;
        }

        if (gate_b) {
            cur = ggml_add(ctx, cur, gate_b);
            cb(cur, "ffn_gate_b", il);
        }
    } else {
        cur = tmp;
    }

    switch (type_op) {
        case LLM_FFN_SILU:
            {
                cur = ggml_silu(ctx, cur);
                cb(cur, "ffn_silu", il);
            } break;
        case LLM_FFN_GELU:
            {
                cur = ggml_gelu(ctx, cur);
                cb(cur, "ffn_gelu", il);
                if (act_scales != NULL) {
                    cur = ggml_div(ctx, cur, act_scales);
                    cb(cur, "ffn_act", il);
                }
            } break;
        case LLM_FFN_RELU:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu", il);
            } break;
        case LLM_FFN_RELU_SQR:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu", il);

                cur = ggml_sqr(ctx, cur);
                cb(cur, "ffn_sqr(relu)", il);
            } break;
    }

    if (type_gate == LLM_FFN_PAR) {
        cur = ggml_mul(ctx, cur, tmp);
        cb(cur, "ffn_gate_par", il);
    }

    cur = ggml_mul_mat(ctx, down, cur);
    if (down_b) {
        cb(cur, "ffn_down", il);
    }

    if (down_b) {
        cur = ggml_add(ctx, cur, down_b);
    }

    return cur;
}

// if max_alibi_bias > 0 then apply ALiBi
static struct ggml_tensor * llm_build_kqv(
        struct ggml_context * ctx,
          const llama_model & model,
        const llama_hparams & hparams,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * wo,
         struct ggml_tensor * wo_b,
         struct ggml_tensor * q_cur,
         struct ggml_tensor * kq_mask,
         struct ggml_tensor * kq_pos,
                    int64_t   n_ctx,
                    int32_t   n_tokens,
                    int32_t   n_kv,
                    float     kq_scale,
         const llm_build_cb & cb,
                    int       il) {
    const int64_t n_head        = hparams.n_head;
    const int64_t n_head_kv     = hparams.n_head_kv;
    const int64_t n_embd_head_k = hparams.n_embd_head_k;
    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
    const int64_t n_embd_head_v = hparams.n_embd_head_v;

    struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
    cb(q, "q", il);

    struct ggml_tensor * k =
        ggml_view_3d(ctx, kv.k_l[il],
                n_embd_head_k, n_kv, n_head_kv,
                ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
                ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
                0);
    cb(k, "k", il);

    struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
    cb(kq, "kq", il);

    if (model.arch == LLM_ARCH_PHI2) {
        // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
        // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
        ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
    }

#if defined(GGML_USE_KOMPUTE)
#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute")
#pragma message("      Falling back to ggml_alibi(). Will become an error in Mar 2024")
#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5488")
    if (hparams.f_max_alibi_bias > 0.0f) {
        kq = ggml_scale(ctx, kq, kq_scale);
        cb(kq, "kq_scaled", il);

        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias);
        cb(kq, "kq_scaled_alibi", il);

        kq = ggml_add(ctx, kq, kq_mask);
        cb(kq, "kq_masked", il);

        kq = ggml_soft_max(ctx, kq);
        cb(kq, "kq_soft_max", il);
    } else
#endif
    {
        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias);
        cb(kq, "kq_soft_max_ext", il);
    }

    GGML_ASSERT(kv.size == n_ctx);

    // split cached v into n_head heads
    struct ggml_tensor * v =
        ggml_view_3d(ctx, kv.v_l[il],
                n_kv, n_embd_head_v, n_head_kv,
                ggml_element_size(kv.v_l[il])*n_ctx,
                ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
                0);
    cb(v, "v", il);

    struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
    cb(kqv, "kqv", il);

    struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
    cb(kqv_merged, "kqv_merged", il);

    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
    cb(cur, "kqv_merged_cont", il);

    ggml_build_forward_expand(graph, cur);

    cur = ggml_mul_mat(ctx, wo, cur);
    if (wo_b) {
        cb(cur, "kqv_wo", il);
    }

    if (wo_b) {
        cur = ggml_add(ctx, cur, wo_b);
    }

    return cur;
}

static struct ggml_tensor * llm_build_kv(
        struct ggml_context * ctx,
          const llama_model & model,
        const llama_hparams & hparams,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * wo,
         struct ggml_tensor * wo_b,
         struct ggml_tensor * k_cur,
         struct ggml_tensor * v_cur,
         struct ggml_tensor * q_cur,
         struct ggml_tensor * kq_mask,
         struct ggml_tensor * kq_pos,
                    int64_t   n_ctx,
                    int32_t   n_tokens,
                    int32_t   kv_head,
                    int32_t   n_kv,
                    float     kq_scale,
         const llm_build_cb & cb,
                    int       il) {

    // these nodes are added to the graph together so that they are not reordered
    // by doing so, the number of splits in the graph is reduced
    ggml_build_forward_expand(graph, q_cur);
    ggml_build_forward_expand(graph, k_cur);
    ggml_build_forward_expand(graph, v_cur);

    llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il);

    struct ggml_tensor * cur;

    cur  = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b,
            q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il);
    cb(cur, "kqv_out", il);

    return cur;
}

struct llm_build_context {
    const llama_model    & model;
    llama_context  & lctx;
    const llama_hparams  & hparams;
    const llama_cparams  & cparams;
    const llama_batch    & batch;
    const llama_kv_cache & kv_self;

    const int64_t n_embd;
    const int64_t n_layer;
    const int64_t n_rot;
    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
    const int64_t n_head;
    const int64_t n_head_kv;
    const int64_t n_embd_head_k;
    const int64_t n_embd_k_gqa;
    const int64_t n_embd_head_v;
    const int64_t n_embd_v_gqa;
    const int64_t n_expert;
    const int64_t n_expert_used;

    const float freq_base;
    const float freq_scale;
    const float ext_factor;
    const float attn_factor;
    const float beta_fast;
    const float beta_slow;
    const float norm_eps;
    const float norm_rms_eps;

    const int32_t n_tokens;
    const int32_t n_kv;     // size of KV cache to consider (n_kv <= n_ctx)
    const int32_t kv_head;  // index of where we store new KV data in the cache
    const int32_t n_orig_ctx;

    const enum llama_pooling_type pooling_type;
    const enum llama_rope_type    rope_type;

    const llm_build_cb & cb;

    std::vector<uint8_t> & buf_compute_meta;

    struct ggml_context * ctx0 = nullptr;

    // TODO: consider making the entire interface noexcept
    llm_build_context(
        llama_context  & lctx,
    const llama_batch  & batch,
    const llm_build_cb & cb,
                  bool   worst_case) :
        model            (lctx.model),
        lctx             (lctx),
        hparams          (model.hparams),
        cparams          (lctx.cparams),
        batch            (batch),
        kv_self          (lctx.kv_self),
        n_embd           (hparams.n_embd),
        n_layer          (hparams.n_layer),
        n_rot            (hparams.n_rot),
        n_ctx            (cparams.n_ctx),
        n_head           (hparams.n_head),
        n_head_kv        (hparams.n_head_kv),
        n_embd_head_k    (hparams.n_embd_head_k),
        n_embd_k_gqa     (hparams.n_embd_k_gqa()),
        n_embd_head_v    (hparams.n_embd_head_v),
        n_embd_v_gqa     (hparams.n_embd_v_gqa()),
        n_expert         (hparams.n_expert),
        n_expert_used    (hparams.n_expert_used),
        freq_base        (cparams.rope_freq_base),
        freq_scale       (cparams.rope_freq_scale),
        ext_factor       (cparams.yarn_ext_factor),
        attn_factor      (cparams.yarn_attn_factor),
        beta_fast        (cparams.yarn_beta_fast),
        beta_slow        (cparams.yarn_beta_slow),
        norm_eps         (hparams.f_norm_eps),
        norm_rms_eps     (hparams.f_norm_rms_eps),
        n_tokens         (batch.n_tokens),
        n_kv             (worst_case ? kv_self.size : kv_self.n),
        kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
        n_orig_ctx       (cparams.n_yarn_orig_ctx),
        pooling_type     (cparams.pooling_type),
        rope_type        (hparams.rope_type),
        cb               (cb),
        buf_compute_meta (lctx.buf_compute_meta) {
            // all initializations should be done in init()
        }

    void init() {
        struct ggml_init_params params = {
            /*.mem_size   =*/ buf_compute_meta.size(),
            /*.mem_buffer =*/ buf_compute_meta.data(),
            /*.no_alloc   =*/ true,
        };

        ctx0 = ggml_init(params);

        lctx.inp_tokens = nullptr;
        lctx.inp_embd = nullptr;
        lctx.inp_pos = nullptr;
        lctx.inp_KQ_mask = nullptr;
        lctx.inp_KQ_pos = nullptr;
        lctx.inp_K_shift = nullptr;
        lctx.inp_mean = nullptr;
        lctx.inp_cls = nullptr;
        lctx.inp_s_copy = nullptr;
        lctx.inp_s_mask = nullptr;
        lctx.inp_s_seq = nullptr;
    }

    void free() {
        if (ctx0) {
            ggml_free(ctx0);
            ctx0 = nullptr;
        }
    }

    struct ggml_cgraph * build_k_shift() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        GGML_ASSERT(kv_self.size == n_ctx);

        lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
        cb(lctx.inp_K_shift, "K_shift", -1);
        ggml_set_input(lctx.inp_K_shift);

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * tmp =
                // we rotate only the first n_rot dimensions
                ggml_rope_custom_inplace(ctx0,
                        ggml_view_3d(ctx0, kv_self.k_l[il],
                            n_embd_head_k, n_head_kv, n_ctx,
                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                            0),
                        lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
            cb(tmp, "K_shifted", il);
            ggml_build_forward_expand(gf, tmp);
        }

        return gf;
    }

    struct ggml_cgraph * build_s_copy() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        GGML_ASSERT(kv_self.recurrent);

        struct ggml_tensor * state_copy = build_inp_s_copy();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);

            conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
            ssm_states  = ggml_get_rows(ctx0,  ssm_states, state_copy);

            // TODO: name the intermediate tensors with cb()

            ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
            ggml_build_forward_expand(gf, ggml_cpy(ctx0,  ssm_states, kv_self.v_l[il]));
        }

        return gf;
    }

    struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        for (uint32_t i = 0; i < ids.size(); ++i) {
            const uint32_t id = ids[i];

            if (i == id || id == ids.size()) {
                continue;
            }

            uint32_t nm = 1;

            while (i + nm < ids.size() && ids[i + nm] == id + nm) {
                nm++;
            }

            for (int il = 0; il < n_layer; ++il) {
                ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
                        n_embd_k_gqa, nm,
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));

                ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
                        n_embd_k_gqa, nm,
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));

                ggml_tensor * view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
                        nm, n_embd_v_gqa,
                        ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
                        ggml_row_size(kv_self.v_l[il]->type, i));

                ggml_tensor * view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
                        nm, n_embd_v_gqa,
                        ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
                        ggml_row_size(kv_self.v_l[il]->type, id));

                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
            }

            i += nm - 1;
        }

        //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);

        return gf;
    }

    struct ggml_tensor * build_inp_pos() {
        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(lctx.inp_pos, "inp_pos", -1);
        ggml_set_input(lctx.inp_pos);
        return lctx.inp_pos;
    }

    struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
        if (causal) {
            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens);
        } else {
            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
        }
        cb(lctx.inp_KQ_mask, "KQ_mask", -1);
        ggml_set_input(lctx.inp_KQ_mask);
        return lctx.inp_KQ_mask;
    }

    struct ggml_tensor * build_inp_KQ_pos() {
        lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv);
        cb(lctx.inp_KQ_pos, "KQ_pos", -1);
        ggml_set_input(lctx.inp_KQ_pos);
        return lctx.inp_KQ_pos;
    }

    struct ggml_tensor * build_inp_mean() {
        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
        cb(lctx.inp_mean, "inp_mean", -1);
        ggml_set_input(lctx.inp_mean);
        return lctx.inp_mean;
    }

    struct ggml_tensor * build_inp_cls() {
        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(lctx.inp_cls, "inp_cls", -1);
        ggml_set_input(lctx.inp_cls);
        return lctx.inp_cls;
    }

    struct ggml_tensor * build_inp_s_copy() {
        lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
        cb(lctx.inp_s_copy, "inp_s_copy", -1);
        ggml_set_input(lctx.inp_s_copy);
        return lctx.inp_s_copy;
    }

    struct ggml_tensor * build_inp_s_mask() {
        lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
        cb(lctx.inp_s_mask, "inp_s_mask", -1);
        ggml_set_input(lctx.inp_s_mask);
        return lctx.inp_s_mask;
    }

    struct ggml_tensor * build_inp_s_seq() {
        lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
        cb(lctx.inp_s_seq, "inp_s_seq", -1);
        ggml_set_input(lctx.inp_s_seq);
        return lctx.inp_s_seq;
    }

    struct ggml_cgraph * build_llama() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            if (model.layers[il].ffn_gate_inp == nullptr) {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            } else {
                // MoE branch
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
                cb(logits, "ffn_moe_logits", il);

                ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
                cb(probs, "ffn_moe_probs", il);

                // select experts
                ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
                cb(selected_experts->src[0], "ffn_moe_argsort", il);

                ggml_tensor * weights = ggml_get_rows(ctx0,
                        ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
                cb(weights, "ffn_moe_weights", il);

                weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]

                ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
                cb(weights_sum, "ffn_moe_weights_sum", il);

                weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
                cb(weights, "ffn_moe_weights_norm", il);

                // compute expert outputs
                ggml_tensor * moe_out = nullptr;

                for (int i = 0; i < n_expert_used; ++i) {
                    ggml_tensor * cur_expert;

                    ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
                    cb(cur_up, "ffn_moe_up", il);

                    ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur);
                    cb(cur_gate, "ffn_moe_gate", il);

                    cur_gate = ggml_silu(ctx0, cur_gate);
                    cb(cur_gate, "ffn_moe_silu", il);

                    cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
                    cb(cur_expert, "ffn_moe_gate_par", il);

                    cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd]
                    cb(cur_expert, "ffn_moe_down", il);

                    cur_expert = ggml_mul(ctx0, cur_expert,
                            ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
                    cb(cur_expert, "ffn_moe_weighted", il);

                    if (i == 0) {
                        moe_out = cur_expert;
                    } else {
                        moe_out = ggml_add(ctx0, moe_out, cur_expert);
                        cb(moe_out, "ffn_moe_out", il);
                    }
                }

                cur = moe_out;
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_baichuan() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        // positions of the tokens in the KV cache
        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                switch (model.type) {
                    case MODEL_7B:
                        Qcur = ggml_rope_custom(
                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                            ext_factor, attn_factor, beta_fast, beta_slow
                        );
                        Kcur = ggml_rope_custom(
                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                            ext_factor, attn_factor, beta_fast, beta_slow
                        );
                        break;
                    case MODEL_13B:
                        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
                        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
                        break;
                    default:
                        GGML_ASSERT(false);
                }
                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);


                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_falcon() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * attn_norm;

            attn_norm = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(attn_norm, "attn_norm", il);

            // self-attention
            {
                if (model.layers[il].attn_norm_2) {
                    // Falcon-40B
                    cur = llm_build_norm(ctx0, inpL, hparams,
                            model.layers[il].attn_norm_2,
                            model.layers[il].attn_norm_2_b,
                            LLM_NORM, cb, il);
                    cb(cur, "attn_norm_2", il);
                } else {
                    cur = attn_norm;
                }

                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                // using mode = 2 for neox mode
                Qcur = ggml_rope_custom(
                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = cur;

            // feed forward
            {
                cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
                        model.layers[il].ffn_up,   NULL,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            cur = ggml_add(ctx0, cur, inpL);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        // norm
        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_starcoder() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
        cb(pos, "pos_embd", -1);

        inpL = ggml_add(ctx0, inpL, pos);
        cb(inpL, "inpL", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_persimmon() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head   == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head/2 == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * residual = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                // split qkv
                GGML_ASSERT(n_head_kv == n_head);

                struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
                cb(tmpqkv, "tmpqkv", il);

                struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
                cb(tmpqkv_perm, "tmpqkv", il);

                struct ggml_tensor * tmpq = ggml_view_3d(
                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
                        ggml_element_size(tmpqkv_perm) * n_embd_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
                        0
                        );
                cb(tmpq, "tmpq", il);

                struct ggml_tensor * tmpk = ggml_view_3d(
                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
                        ggml_element_size(tmpqkv_perm) * n_embd_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
                        );
                cb(tmpk, "tmpk", il);

                // Q/K Layernorm
                tmpq = llm_build_norm(ctx0, tmpq, hparams,
                        model.layers[il].attn_q_norm,
                        model.layers[il].attn_q_norm_b,
                        LLM_NORM, cb, il);
                cb(tmpq, "tmpq", il);

                tmpk = llm_build_norm(ctx0, tmpk, hparams,
                        model.layers[il].attn_k_norm,
                        model.layers[il].attn_k_norm_b,
                        LLM_NORM, cb, il);
                cb(tmpk, "tmpk", il);

                // RoPE the first n_rot of q/k, pass the other half, and concat.
                struct ggml_tensor * qrot = ggml_view_3d(
                        ctx0, tmpq, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpq) * n_embd_head,
                        ggml_element_size(tmpq) * n_embd_head * n_head,
                        0
                        );
                cb(qrot, "qrot", il);

                struct ggml_tensor * krot = ggml_view_3d(
                        ctx0, tmpk, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpk) * n_embd_head,
                        ggml_element_size(tmpk) * n_embd_head * n_head,
                        0
                        );
                cb(krot, "krot", il);

                // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
                struct ggml_tensor * qpass = ggml_view_3d(
                        ctx0, tmpq, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpq) * n_embd_head,
                        ggml_element_size(tmpq) * n_embd_head * n_head,
                        ggml_element_size(tmpq) * n_rot
                        );
                cb(qpass, "qpass", il);

                struct ggml_tensor * kpass = ggml_view_3d(
                        ctx0, tmpk, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpk) * n_embd_head,
                        ggml_element_size(tmpk) * n_embd_head * n_head,
                        ggml_element_size(tmpk) * n_rot
                        );
                cb(kpass, "kpass", il);

                struct ggml_tensor * qrotated = ggml_rope_custom(
                    ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(qrotated, "qrotated", il);

                struct ggml_tensor * krotated = ggml_rope_custom(
                    ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(krotated, "krotated", il);

                // ggml currently only supports concatenation on dim=2
                // so we need to permute qrot, qpass, concat, then permute back.
                qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
                cb(qrotated, "qrotated", il);

                krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
                cb(krotated, "krotated", il);

                qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
                cb(qpass, "qpass", il);

                kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
                cb(kpass, "kpass", il);

                struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
                cb(Q, "Q", il);

                Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_view_3d(
                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
                        ggml_element_size(tmpqkv_perm) * n_embd_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
                        );
                cb(Vcur, "Vcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_refact() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        // positions of the tokens in the KV cache
        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                cb(Kcur, "Kcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                cb(Qcur, "Qcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_bert() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();

        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        struct ggml_tensor * inp_pos  = build_inp_pos();
        struct ggml_tensor * inp_mean = build_inp_mean();
        struct ggml_tensor * inp_cls  = build_inp_cls();

        // construct input embeddings (token, type, position)
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // token types are hardcoded to zero ("Sentence A")
        struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
        inpL = ggml_add(ctx0, inpL, type_row0);
        if (model.arch == LLM_ARCH_BERT) {
            inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
        }
        cb(inpL, "inp_embd", -1);

        // embed layer norm
        inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
        cb(inpL, "inp_norm", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);

        // iterate layers
        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * cur = inpL;

            struct ggml_tensor * Qcur;
            struct ggml_tensor * Kcur;
            struct ggml_tensor * Vcur;

            // self-attention
            if (model.arch == LLM_ARCH_BERT) {
                Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
                cb(Qcur, "Qcur", il);

                Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
                cb(Kcur, "Kcur", il);

                Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
            } else {
                // compute Q and K and RoPE them
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
            struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));

            struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
            cb(kq, "kq", il);

            kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
            cb(kq, "kq_soft_max_ext", il);

            struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
            cb(v, "v", il);

            struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
            cb(kqv, "kqv", il);

            struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
            cb(kqv_merged, "kqv_merged", il);

            cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
            cb(cur, "kqv_merged_cont", il);

            ggml_build_forward_expand(gf, cur);

            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
            if (model.layers[il].bo) {
                cb(cur, "kqv_wo", il);
            }

            if (model.layers[il].bo) {
                cur = ggml_add(ctx0, cur, model.layers[il].bo);
            }
            cb(cur, "kqv_out", il);

            // re-add the layer input
            cur = ggml_add(ctx0, cur, inpL);

            // attention layer norm
            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);

            struct ggml_tensor * ffn_inp = cur;
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            if (model.arch == LLM_ARCH_BERT) {
                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
            } else {
                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            }
            cb(cur, "ffn_out", il);

            // attentions bypass the intermediate layer
            cur = ggml_add(ctx0, cur, ffn_inp);

            // output layer norm
            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);

            // input for next layer
            inpL = cur;
        }

        // final output
        cur = inpL;
        cb(cur, "result_embd", -1);

        // pooling layer
        switch (pooling_type) {
            case LLAMA_POOLING_TYPE_NONE:
                {
                    // nop
                } break;
            case LLAMA_POOLING_TYPE_MEAN:
                {
                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
                    cb(cur, "result_embd_pooled", -1);
                } break;
            case LLAMA_POOLING_TYPE_CLS:
                {
                    cur = ggml_get_rows(ctx0, cur, inp_cls);
                    cb(cur, "result_embd_pooled", -1);
                } break;
            case LLAMA_POOLING_TYPE_UNSPECIFIED:
                {
                    GGML_ASSERT(false && "Invalid pooling type");
                } break;
        }

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_bloom() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        // positions of the tokens in the KV cache
        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();

        inpL = llm_build_norm(ctx0, inpL, hparams,
                model.tok_norm,
                model.tok_norm_b,
                LLM_NORM, cb, -1);
        cb(inpL, "inp_norm", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_mpt() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        // positions of the tokens in the KV cache
        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * attn_norm;

            attn_norm = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(attn_norm, "attn_norm", il);

            // self-attention
            {
                cur = attn_norm;

                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                if (model.layers[il].bqkv){
                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                    cb(cur, "bqkv", il);
                }

                if (hparams.f_clamp_kqv > 0.0f) {
                    cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                    cb(cur, "wqkv_clamped", il);
                }

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // feed forward
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);
                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        model.layers[il].ffn_act,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_stablelm() {
        struct ggml_cgraph * gf = ggml_new_graph(ctx0);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_qwen() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                // using mode = 2 for neox mode
                Qcur = ggml_rope_custom(
                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward forward
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_qwen2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                cb(Vcur, "Vcur", il);

                // these nodes are added to the graph together so that they are not reordered
                // by doing so, the number of splits in the graph is reduced
                ggml_build_forward_expand(gf, Qcur);
                ggml_build_forward_expand(gf, Kcur);
                ggml_build_forward_expand(gf, Vcur);

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, cur,
                    model.layers[il].ffn_up,   NULL,
                    model.layers[il].ffn_gate, NULL,
                    model.layers[il].ffn_down, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_phi2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * attn_norm_output;
        struct ggml_tensor * ffn_output;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(attn_norm_output, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = nullptr;
                struct ggml_tensor * Kcur = nullptr;
                struct ggml_tensor * Vcur = nullptr;

                if (model.layers[il].wqkv) {
                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
                    cb(cur, "wqkv", il);

                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                    cb(cur, "bqkv", il);

                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
                } else {
                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                }

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                Qcur = ggml_rope_custom(
                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                // with phi2, we scale the Q to avoid precision issues
                // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
                Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            // FF
            {
                ffn_output = llm_build_ffn(ctx0, attn_norm_output,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(ffn_output, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_output);
            cb(cur, "l_out", il);

            cur = ggml_add(ctx0, cur, inpL);
            cb(cur, "l_out", il);

            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output_no_bias", -1);

        cur = ggml_add(ctx0, cur, model.output_b);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_plamo() {
        struct ggml_cgraph * gf = ggml_new_graph(ctx0);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            struct ggml_tensor * attention_norm = cur;

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_custom(
                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos,
                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos,
                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }
            struct ggml_tensor * sa_out = cur;

            cur = attention_norm;

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up, NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, sa_out);
            cb(cur, "l_out", il);

            cur = ggml_add(ctx0, cur, inpL);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_gpt2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * pos;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
        cb(pos, "pos_embd", -1);

        inpL = ggml_add(ctx0, inpL, pos);
        cb(inpL, "inpL", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_codeshell() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(tmpq, "tmpq", il);
                cb(tmpk, "tmpk", il);
                cb(Vcur, "Vcur", il);

                struct ggml_tensor * Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_orion() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                // if (model.layers[il].bq) {
                //     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                //     cb(Qcur, "Qcur", il);
                // }

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                // if (model.layers[il].bk) {
                //     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                //     cb(Kcur, "Kcur", il);
                // }

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                // if (model.layers[il].bv) {
                //     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                //     cb(Vcur, "Vcur", il);
                // }

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, cur,
                    model.layers[il].ffn_up,   NULL,
                    model.layers[il].ffn_gate, NULL,
                    model.layers[il].ffn_down, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_internlm2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, cur,
                    model.layers[il].ffn_up,   NULL,
                    model.layers[il].ffn_gate, NULL,
                    model.layers[il].ffn_down, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    // ref: https://arxiv.org/abs/2203.03466
    //      https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
    // based on the original build_llama() function
    struct ggml_cgraph * build_minicpm() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        const int64_t n_embd = hparams.n_embd;
        //TODO: if the model varies, these parameters need to be read from the model
        const int64_t n_embd_base = 256;
        const float scale_embd  = 12.0f;
        const float scale_depth = 1.4f;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // scale the input embeddings
        inpL = ggml_scale(ctx0, inpL, scale_embd);
        cb(inpL, "inp_scaled", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            // scale_res - scale the hidden states for residual connection
            const float scale_res = scale_depth/sqrtf(float(n_layer));
            cur = ggml_scale(ctx0, cur, scale_res);
            cb(cur, "hidden_scaled", -1);

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            // scale the hidden states for residual connection
            cur = ggml_scale(ctx0, cur, scale_res);
            cb(cur, "hidden_scaled_ffn", -1);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head scaling
        const float scale_lmhead = float(n_embd_base)/float(n_embd);
        cur = ggml_scale(ctx0, cur, scale_lmhead);
        cb(cur, "lmhead_scaling", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_gemma() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head_k = hparams.n_embd_head_k;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
        cb(inpL, "inp_scaled", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_custom(
                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos,
                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Qcur, "Qcur", il);

                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
                cb(Qcur, "Qcur_scaled", il);

                Kcur = ggml_rope_custom(
                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
            cb(sa_out, "sa_out", il);

            cur = llm_build_norm(ctx0, sa_out, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up, NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, sa_out);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_starcoder2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                cb(cur, "kqv_out", il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network

            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
            cb(cur, "ffn_out", il);
            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_mamba() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t d_model = n_embd;
        const int64_t d_conv  = hparams.ssm_d_conv;
        const int64_t d_inner = hparams.ssm_d_inner;
        GGML_ASSERT(2 * d_model == d_inner);
        const int64_t d_state = hparams.ssm_d_state;
        const int64_t dt_rank = hparams.ssm_dt_rank;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        // {n_embd, n_tokens}
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

        struct ggml_tensor * state_mask = build_inp_s_mask();
        struct ggml_tensor * state_seq  = build_inp_s_seq();

        for (int il = 0; il < n_layer; ++il) {
            // (ab)using the KV cache to store the states
            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);

            // clear states of sequences which are starting at the beginning of this batch
            {
                conv_states = ggml_mul(ctx0,
                    ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]),
                    state_mask);
                ssm_states  = ggml_mul(ctx0,
                    ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]),
                    state_mask);
            }

            conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv);
            ssm_states  = ggml_reshape_3d(ctx0,  ssm_states,    d_state, d_inner, n_kv);

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
            struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
            // split the above in two
            // => {d_inner, n_tokens}
            struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
            struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner);

            // conv
            {
                // Custom operator which is needed only to ease simultaneous sequence processing.
                // For a single sequence, the equivalent is to concatenate the columns of conv_states and x,
                // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension,
                // then element-wise multiply that with the conv1d weigth,
                // then sum the elements of each row,
                // (the last two steps are a dot product over rows (also doable with mul_mat))
                // then permute away the ne[0] dimension,
                // and then you're left with the resulting x tensor.
                // The new conv_states is the last (d_conv - 1) columns
                // of the last 3rd dimensional "layer" of the self-overlapping view.
                // For simultaneous sequences, it's more complicated.
                struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq);

                // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache
                ggml_build_forward_expand(gf,
                    ggml_cpy(ctx0,
                        ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)),
                        ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_self.head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv))));

                // extract x from x_conv
                x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0);

                // bias
                x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b);

                x = ggml_silu(ctx0, x);
            }

            // ssm
            {
                // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
                struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
                // split
                struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
                struct ggml_tensor * B  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
                struct ggml_tensor * C  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));

                // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
                dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
                dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);

                // Custom operator to optimize the parallel associative scan
                // as described in the Annex D of the Mamba paper.
                // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined,
                // because only a single tensor can be returned.
                struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq);

                // store last states (the second part of y_ssm_states)
                ggml_build_forward_expand(gf,
                    ggml_cpy(ctx0,
                        ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)),
                        ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_self.head*d_state*d_inner*ggml_element_size(ssm_states))));

                struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0);

                // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens}
                y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
                y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));

                // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
                cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
            }

            // residual
            cur = ggml_add(ctx0, cur, inpL);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        // final rmsnorm
        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }
};

static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
    llama_batch dummy;
    dummy.n_tokens = 0;

    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };

    struct llm_build_context llm(lctx, dummy, cb, false);

    llm.init();

    struct ggml_cgraph * result = llm.build_defrag(ids);

    llm.free();

    return result;
}

static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
    llama_batch dummy;
    dummy.n_tokens = 0;

    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };

    struct llm_build_context llm(lctx, dummy, cb, false);

    llm.init();

    struct ggml_cgraph * result = llm.build_k_shift();

    llm.free();

    return result;
}

static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
    llama_batch dummy;
    dummy.n_tokens = 0;

    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };

    struct llm_build_context llm(lctx, dummy, cb, false);

    llm.init();

    struct ggml_cgraph * result = llm.build_s_copy();

    llm.free();

    return result;
}

static struct ggml_cgraph * llama_build_graph(
         llama_context & lctx,
     const llama_batch & batch,
                  bool   worst_case) {
    const auto & model = lctx.model;

    // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
    llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
        if (il >= 0) {
            ggml_format_name(cur, "%s-%d", name, il);
        } else {
            ggml_set_name(cur, name);
        }

        if (!lctx.cparams.offload_kqv) {
            if (strcmp(name, "kqv_merged_cont") == 0) {
                // all nodes between the KV store and the attention output are run on the CPU
                ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
            }
        }

        if (il != -1) {
            for (auto * backend : lctx.backends) {
                if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
                    ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
                    break;
                }
            }
        }
    };

    struct ggml_cgraph * result = NULL;

    struct llm_build_context llm(lctx, batch, cb, worst_case);

    llm.init();

    switch (model.arch) {
        case LLM_ARCH_LLAMA:
            {
                result = llm.build_llama();
            } break;
        case LLM_ARCH_BAICHUAN:
            {
                result = llm.build_baichuan();
            } break;
        case LLM_ARCH_FALCON:
            {
                result = llm.build_falcon();
            } break;
        case LLM_ARCH_STARCODER:
            {
                result = llm.build_starcoder();
            } break;
        case LLM_ARCH_PERSIMMON:
            {
                result = llm.build_persimmon();
            } break;
        case LLM_ARCH_REFACT:
            {
                result = llm.build_refact();
            } break;
        case LLM_ARCH_BERT:
        case LLM_ARCH_NOMIC_BERT:
            {
                result = llm.build_bert();
            } break;
        case LLM_ARCH_BLOOM:
            {
                result = llm.build_bloom();
            } break;
        case LLM_ARCH_MPT:
            {
                result = llm.build_mpt();
            } break;
         case LLM_ARCH_STABLELM:
            {
                result = llm.build_stablelm();
            } break;
        case LLM_ARCH_QWEN:
            {
                result = llm.build_qwen();
            } break;
        case LLM_ARCH_QWEN2:
            {
                result = llm.build_qwen2();
            } break;
        case LLM_ARCH_PHI2:
            {
                result = llm.build_phi2();
            } break;
        case LLM_ARCH_PLAMO:
            {
                result = llm.build_plamo();
            } break;
        case LLM_ARCH_GPT2:
            {
                result = llm.build_gpt2();
            } break;
        case LLM_ARCH_CODESHELL:
            {
                result = llm.build_codeshell();
            } break;
        case LLM_ARCH_ORION:
            {
                result = llm.build_orion();
            } break;
        case LLM_ARCH_INTERNLM2:
            {
                result = llm.build_internlm2();
            } break;
        case LLM_ARCH_MINICPM:
            {
                result = llm.build_minicpm();
            } break;
        case LLM_ARCH_GEMMA:
            {
                result = llm.build_gemma();
            } break;
        case LLM_ARCH_STARCODER2:
            {
                result = llm.build_starcoder2();
            } break;
        case LLM_ARCH_MAMBA:
            {
                result = llm.build_mamba();
            } break;
        default:
            GGML_ASSERT(false);
    }

    llm.free();

    return result;
}

static void llama_set_k_shift(llama_context & lctx) {
    const int64_t kv_size = lctx.kv_self.size;

    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));

    int32_t * data = (int32_t *) lctx.inp_K_shift->data;

    for (int i = 0; i < kv_size; ++i) {
        data[i] = lctx.kv_self.cells[i].delta;
    }
}

static void llama_set_s_copy(llama_context & lctx) {
    const int64_t kv_size = lctx.kv_self.size;

    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));

    int32_t * data = (int32_t *) lctx.inp_s_copy->data;

    for (int i = 0; i < kv_size; ++i) {
        data[i] = lctx.kv_self.cells[i].src;
    }
}

static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
    //
    // set input data
    //

    const auto & hparams = lctx.model.hparams;
    const auto & cparams = lctx.cparams;
    const auto & kv_self = lctx.kv_self;

    if (batch.token) {
        const int64_t n_tokens = batch.n_tokens;

        ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
    }

    if (batch.embd) {
        const int64_t n_embd   = hparams.n_embd;
        const int64_t n_tokens = batch.n_tokens;

        ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
    }

    if (batch.pos && lctx.inp_pos) {
        const int64_t n_tokens = batch.n_tokens;

        ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
    }

    GGML_ASSERT(
        (hparams.causal_attn || !cparams.causal_attn) &&
        "non-causal attention with generative models is not supported"
    );

    if (lctx.inp_KQ_mask) {
        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
        if (cparams.causal_attn) {
            const int64_t n_kv     = kv_self.n;
            const int64_t n_tokens = batch.n_tokens;

            assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));

            float * data = (float *) lctx.inp_KQ_mask->data;

            // For causal attention, use only the previous KV cells
            // of the correct sequence for each token of the batch.
            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
            for (int h = 0; h < 1; ++h) {
                for (int j = 0; j < n_tokens; ++j) {
                    const llama_pos    pos    = batch.pos[j];
                    const llama_seq_id seq_id = batch.seq_id[j][0];

                    for (int i = 0; i < n_kv; ++i) {
                        float f;
                        if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
                            f = -INFINITY;
                        } else {
                            f = 0.0f;
                        }
                        data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
                    }
                }
            }
        } else {
            // when using kv cache, the mask needs to match the kv cache size
            const int64_t n_tokens = batch.n_tokens;
            const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;

            assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));

            float * data = (float *) lctx.inp_KQ_mask->data;

            for (int h = 0; h < 1; ++h) {
                for (int j = 0; j < n_tokens; ++j) {
                    const llama_seq_id seq_id = batch.seq_id[j][0];

                    for (int i = 0; i < n_tokens; ++i) {
                        float f = -INFINITY;
                        for (int s = 0; s < batch.n_seq_id[i]; ++s) {
                            if (batch.seq_id[i][s] == seq_id) {
                                f = 0.0f;
                                break;
                            }
                        }

                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
                    }

                    for (int i = n_tokens; i < n_stride; ++i) {
                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
                    }
                }
            }
        }
    }

    if (hparams.need_kq_pos) {
        const int64_t n_kv = kv_self.n;

        assert(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer));

        float * data = (float *) lctx.inp_KQ_pos->data;

        for (int i = 0; i < n_kv; ++i) {
            data[i] = float(lctx.kv_self.cells[i].pos);
        }
    }

    if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
        const int64_t n_tokens = batch.n_tokens;

        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));

        float * data = (float *) lctx.inp_mean->data;
        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));

        std::vector<uint64_t> sum(n_tokens, 0);
        for (int i = 0; i < n_tokens; ++i) {
            const llama_seq_id seq_id = batch.seq_id[i][0];

            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");

            sum[seq_id] += 1;
        }

        std::vector<float> div(n_tokens, 0.0f);
        for (int i = 0; i < n_tokens; ++i) {
            const uint64_t s = sum[i];
            if (s > 0) {
                div[i] = 1.0f/float(s);
            }
        }

        for (int i = 0; i < n_tokens; ++i) {
            const llama_seq_id seq_id = batch.seq_id[i][0];
            data[seq_id*n_tokens + i] = div[seq_id];
        }
    }

    if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
        const int64_t n_tokens = batch.n_tokens;

        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));

        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));

        for (int i = 0; i < n_tokens; ++i) {
            const llama_seq_id seq_id = batch.seq_id[i][0];
            const llama_pos    pos    = batch.pos[i];

            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");

            if (pos == 0) {
                data[seq_id] = i;
            }
        }
    }

    if (kv_self.recurrent) {
        const int64_t n_kv = kv_self.n;

        {
            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
            float * data = (float *) lctx.inp_s_mask->data;

            // states which are not affected by the current batch are left untouched
            for (int i = 0; i < n_kv; ++i) {
                llama_seq_id    seq_id       = i + lctx.kv_self.head;
                llama_kv_cell & kv_cell      = lctx.kv_self.cells[seq_id];
                bool            has_self_seq = kv_cell.has_seq_id(seq_id);

                data[i] = (float) has_self_seq;

                // ensure current sequences will be kept
                if (!has_self_seq && kv_cell.pos >= 0) {
                    kv_cell.seq_id.insert(seq_id);
                }
            }
        }
        // For Mamba (and other recurrent architectures),
        // update the correct state(s)/sequence(s) for each token of the batch.
        // Like with the KQ_mask, if a token in the batch has multiple sequences,
        // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
        {
            const int64_t n_tokens = batch.n_tokens;

            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
            int32_t * data = (int32_t *) lctx.inp_s_seq->data;

            for (int j = 0; j < n_tokens; ++j) {
                const int32_t n_seq = batch.n_seq_id[j];
                GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence

                for (int i = 0; i < n_kv; ++i) {
                    if (i < n_seq) {
                        // for this type of model, the head is the minimum seq_id of the batch
                        data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
                    } else {
                        data[j*n_kv + i] = -1;
                    }
                }
            }
        }
    }
}

static void llama_graph_compute(
        llama_context & lctx,
          ggml_cgraph * gf,
                  int   n_threads) {
#ifdef GGML_USE_MPI
    const int64_t n_layer = lctx.model.hparams.n_layer;
    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
#endif

#ifdef GGML_USE_METAL
    if (ggml_backend_is_metal(lctx.backend_metal)) {
        ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
    }
#endif

    if (lctx.backend_cpu != nullptr) {
        ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
        ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
    }

    ggml_backend_sched_graph_compute_async(lctx.sched, gf);

    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));

#ifdef GGML_USE_MPI
    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
#endif
}

// decode a batch of tokens by evaluating the transformer
//
//   - lctx:      llama context
//   - batch:     batch to evaluate
//
// return 0 on success
// return positive int on warning
// return negative int on error
//
static int llama_decode_internal(
         llama_context & lctx,
           llama_batch   batch_all) { // TODO: rename back to batch

    const uint32_t n_tokens_all = batch_all.n_tokens;

    if (n_tokens_all == 0) {
        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
        return -1;
    }

    const auto & model   = lctx.model;
    const auto & hparams = model.hparams;
    const auto & cparams = lctx.cparams;

    GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT

    GGML_ASSERT(n_tokens_all <= cparams.n_ctx);

    //const int64_t t_start_us = ggml_time_us();
    if (lctx.t_compute_start_us == 0) {
        lctx.t_compute_start_us = ggml_time_us();
    }
    lctx.n_queued_tokens += n_tokens_all;

#ifdef GGML_USE_MPI
    // TODO: needs fix after #3228
    GGML_ASSERT(false && "not implemented");
    //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif

    auto & kv_self = lctx.kv_self;

    const int64_t n_embd  = hparams.n_embd;
    const int64_t n_vocab = hparams.n_vocab;


    auto * logits_out = lctx.logits;

#ifndef NDEBUG
    auto & logits_valid = lctx.logits_valid;
    logits_valid.clear();
    logits_valid.resize(n_tokens_all);

    memset(logits_out, 0, lctx.logits_size*sizeof(float));
#endif

    const auto n_ubatch = cparams.n_ubatch;

    std::vector<llama_pos> pos;
    std::vector<int32_t>                   n_seq_id;
    std::vector<llama_seq_id *>            seq_id_arr;
    std::vector<std::vector<llama_seq_id>> seq_id;

    for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
        const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
        llama_batch u_batch = {
            /* .n_tokens   = */ (int32_t) n_tokens,
            /* .token      = */ batch_all.token     ? batch_all.token    + cur_token        : nullptr,
            /* .embd       = */ batch_all.embd      ? batch_all.embd     + cur_token*n_embd : nullptr,
            /* .pos        = */ batch_all.pos       ? batch_all.pos      + cur_token        : nullptr,
            /* .n_seq_id   = */ batch_all.n_seq_id  ? batch_all.n_seq_id + cur_token        : nullptr,
            /* .seq_id     = */ batch_all.seq_id    ? batch_all.seq_id   + cur_token        : nullptr,
            /* .logits     = */ batch_all.logits    ? batch_all.logits   + cur_token        : nullptr,
            /* .all_pos_0  = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
            /* .all_pos_1  = */ batch_all.all_pos_1,
            /* .all_seq_id = */ batch_all.all_seq_id,
        };

        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
        GGML_ASSERT(n_threads > 0);

        // helpers for smoother batch API transition
        // after deprecating the llama_eval calls, these will be removed
        if (u_batch.pos == nullptr) {
            pos.resize(n_tokens);
            for (uint32_t i = 0; i < n_tokens; i++) {
                pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
            }

            u_batch.pos = pos.data();
        }

        if (u_batch.seq_id == nullptr) {
            n_seq_id.resize(n_tokens);
            seq_id.resize(n_tokens);
            seq_id_arr.resize(n_tokens);
            for (uint32_t i = 0; i < n_tokens; i++) {
                n_seq_id[i] = 1;
                seq_id[i].resize(1);
                seq_id[i][0] = u_batch.all_seq_id;
                seq_id_arr[i] = seq_id[i].data();
            }

            u_batch.n_seq_id = n_seq_id.data();
            u_batch.seq_id = seq_id_arr.data();
        }

        // non-causal masks do not use the KV cache
        if (hparams.causal_attn) {
            llama_kv_cache_update(&lctx);

            // if we have enough unused cells before the current head ->
            //   better to start searching from the beginning of the cache, hoping to fill it
            if (kv_self.head > kv_self.used + 2*n_tokens) {
                kv_self.head = 0;
            }

            if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
                return 1;
            }

            if (!kv_self.recurrent) {
                // a heuristic, to avoid attending the full cache if it is not yet utilized
                // after enough generations, the benefit from this heuristic disappears
                // if we start defragmenting the cache, the benefit from this will be more important
                kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
                //kv_self.n = llama_kv_cache_cell_max(kv_self);
            }
        }

        //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);

        ggml_backend_sched_reset(lctx.sched);
        ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);

        ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);

        // the output is always the last tensor in the graph
        struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
        struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];

        if (!hparams.causal_attn) {
            res = nullptr; // do not extract logits for embedding models such as BERT

            // token or sequence embeddings
            embd = gf->nodes[gf->n_nodes - 1];

            GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
        } else {
            if (strcmp(res->name, "result_output") == 0) {
                // the token embeddings could be the second to last tensor, or the third to last tensor
                if (strcmp(embd->name, "result_norm") != 0) {
                    embd = gf->nodes[gf->n_nodes - 3];
                    GGML_ASSERT(strcmp(embd->name, "result_norm") == 0);
                }
            } else {
                GGML_ASSERT(false && "missing result_output tensor");
            }
        }
        // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);

        // for big prompts, if BLAS is enabled, it is better to use only one thread
        // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
        // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
        //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
        //       with the BLAS calls. need a better solution
        // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
        //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
        if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
            n_threads = std::min(4, n_threads);
        }

        ggml_backend_sched_alloc_graph(lctx.sched, gf);

        llama_set_inputs(lctx, u_batch);

        llama_graph_compute(lctx, gf, n_threads);

        // update the kv ring buffer
        {
            kv_self.head += n_tokens;

            // Ensure kv cache head points to a valid index.
            if (kv_self.head >= kv_self.size) {
                kv_self.head = 0;
            }
        }

#ifdef GGML_PERF
        // print timing information per ggml operation (for debugging purposes)
        // requires GGML_PERF to be defined
        ggml_graph_print(gf);
#endif

        // plot the computation graph in dot format (for debugging purposes)
        //if (n_past%100 == 0) {
        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
        //}

        // extract logits
        // TODO: do not compute and extract logits if only embeddings are needed
        //       update the graphs to skip "result_output" if logits are not needed
        if (res) {
            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
            GGML_ASSERT(backend_res != nullptr);
            if (u_batch.logits) {
                int32_t i_first = -1;
                for (uint32_t i = 0; i < n_tokens; i++) {
                    if (u_batch.logits[i] && i_first == -1) {
                        i_first = (int32_t) i;
                    }
                    if (u_batch.logits[i] == 0 || i == n_tokens - 1) {
                        if (i_first != -1) {
                            int i_last = u_batch.logits[i] == 0 ? i : i + 1;
                            // extract logits for the range [i_first, i_last)
                            // group the requests to minimize the number of calls to the backend
                            ggml_backend_tensor_get_async(backend_res, res,
                                logits_out + n_vocab*(cur_token + i_first),
                                i_first*n_vocab*sizeof(float),
                                (i_last - i_first)*n_vocab*sizeof(float));
                            i_first = -1;
                        }
                    }
#ifndef NDEBUG
                    logits_valid[cur_token + i] = u_batch.logits[i] != 0;;
#endif
                }
            } else if (lctx.logits_all) {
                ggml_backend_tensor_get_async(backend_res, res, logits_out + n_vocab*cur_token, 0, n_vocab*n_tokens*sizeof(float));
#ifndef NDEBUG
                std::fill(logits_valid.begin() + cur_token, logits_valid.begin() + cur_token + n_tokens, true);
#endif
            } else {
                if (cur_token + n_tokens >= n_tokens_all) {
                    ggml_backend_tensor_get_async(backend_res, res, logits_out, n_vocab*(n_tokens - 1)*sizeof(float), n_vocab*sizeof(float));
#ifndef NDEBUG
                    logits_valid[0] = true;
#endif
                }
            }
        }

        // extract embeddings
        if (cparams.embeddings && embd) {
            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
            GGML_ASSERT(backend_embd != nullptr);

            switch (cparams.pooling_type) {
                case LLAMA_POOLING_TYPE_NONE:
                    {
                        // extract token embeddings
                        auto & embd_out = lctx.embd;

                        if (u_batch.logits) {
                            //embd_out.resize(n_embd * n_tokens);
                            for (uint32_t i = 0; i < n_tokens; i++) {
                                if (u_batch.logits[i] == 0) {
                                    continue;
                                }
                                ggml_backend_tensor_get_async(backend_embd, embd, embd_out + n_embd*(i + cur_token), (n_embd*i)*sizeof(float), n_embd*sizeof(float));
                            }
                        }
                    } break;
                case LLAMA_POOLING_TYPE_CLS:
                case LLAMA_POOLING_TYPE_MEAN:
                    {
                        // FIXME: this may not work if the sequences are split into different batches
                        GGML_ASSERT(n_tokens_all == n_tokens);

                        GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);

                        // extract sequence embeddings
                        auto & embd_seq_out = lctx.embd_seq;
                        embd_seq_out.clear();

                        for (uint32_t i = 0; i < n_tokens; i++) {
                            const llama_seq_id seq_id = u_batch.seq_id[i][0];
                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
                                continue;
                            }
                            embd_seq_out[seq_id].resize(n_embd);
                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
                        }
                    } break;
                case LLAMA_POOLING_TYPE_UNSPECIFIED:
                    {
                        GGML_ASSERT(false && "unknown pooling type");
                    } break;
            }
        }
    }

    // wait for the computation to finish (automatically done when obtaining the model output)
    //llama_synchronize(&lctx);

    // decide if we need to defrag the kv cache
    if (cparams.defrag_thold >= 0.0f) {
        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used + n_tokens_all)/float(kv_self.n) : 0.0f;

        // queue defragmentation for next llama_kv_cache_update
        if (fragmentation > cparams.defrag_thold) {
            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);

            llama_kv_cache_defrag(kv_self);
        }
    }

    return 0;
}


// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
    auto & kv_self = lctx.kv_self;

    const auto & hparams = lctx.model.hparams;

    const uint32_t n_layer = hparams.n_layer;

    const uint32_t n_kv   = llama_kv_cache_cell_max(kv_self);
    const uint32_t n_used = kv_self.used;

    assert(n_used <= n_kv);

    //const int64_t t_start = ggml_time_us();

    // number of cells moved
    uint32_t n_moves = 0;

    // determine which KV cells to move where
    //
    //  cell i moves to ids[i]
    //
    //  if ids[i] == i || ids[i] == n_kv, then cell i is not moved
    //
    std::vector<uint32_t> ids(n_kv, n_kv);

    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
        const auto & cell0 = kv_self.cells[i0];

        if (!cell0.is_empty()) {
            ids[i0] = i0;

            continue;
        }

        // found a hole - fill it with data from the end of the cache

        uint32_t nh = 1;

        // determine the size of the hole
        while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
            nh++;
        }

        // each move requires 6*n_layer tensors (see build_defrag)
        //   - source view, destination view, copy operation
        //   - x2 for keys and values
        //
        if (6*(n_moves + nh)*n_layer >= LLAMA_MAX_NODES) {
            // the graph is too big, we cannot move more cells
            break;
        }

        uint32_t nf = 0;
        uint32_t is = n_kv - 1;

        // starting from the end, find nh non-empty cells
        for (; is > i0; --is) {
            const auto & cell1 = kv_self.cells[is];

            if (cell1.is_empty() || ids[is] != n_kv) {
                continue;
            }

            // non-empty cell which is not yet moved
            nf++;

            if (nf == nh) {
                break;
            }
        }

        // this can only happen if `n_used` is not accurate, which would be a bug
        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");

        nf = 0;

        uint32_t i1 = is;

        // are we moving a continuous block of memory?
        bool cont = false;

        // go back and move the nf cells to the hole
        for (; i1 < n_kv; ++i1) {
            auto & cell1 = kv_self.cells[i1];

            if (cell1.is_empty() || ids[i1] != n_kv) {
                cont = false;
                continue;
            }

            // this cell goes to (i0 + nf)
            ids[i1] = i0 + nf;

            // move the cell meta data
            kv_self.cells[i0 + nf] = cell1;

            // clear the old cell and move the head there
            cell1 = llama_kv_cell();
            kv_self.head = n_used;

            if (!cont) {
                n_moves++;
                cont = true;
            }

            nf++;

            if (nf == nh) {
                break;
            }
        }

        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);

        i0 += nh - 1;
    }

    if (n_moves == 0) {
        return;
    }

    //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);

    //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);

#if 0
    // CPU defrag
    //
    // TODO: optimizations are possible:
    //       - multiple threads
    //       - avoid copying to the host memory when already there
    //
    // likely not worth the effort, as we have ggml_graph based defrag
    //

    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();

    const uint32_t kv_size = kv_self.size;

    std::vector<uint8_t> buf_k;
    std::vector<uint8_t> buf_v;

    for (uint32_t il = 0; il < n_layer; ++il) {
        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
        const size_t k_size     = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);

        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
        const size_t v_size    = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);

        buf_k.resize(k_size);
        buf_v.resize(v_size);

        ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
        ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());

        // batch move [i, i+nm) to [id, id+nm)
        // note: cells can move only to a lower index
        for (uint32_t i = 0; i < n_kv; ++i) {
            const uint32_t id = ids[i];

            if (i == id || id == n_kv) {
                continue;
            }

            uint32_t nm = 1;

            while (i + nm < n_kv && ids[i + nm] == id + nm) {
                nm++;
            }

            // move keys
            {
                const int64_t os =  i*k_size_row;
                const int64_t od = id*k_size_row;

                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
            }

            // move values (note: they are transposed)
            {
                const int64_t os =  i;
                const int64_t od = id;

                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
                }
            }

            i += nm - 1;
        }

        ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
        ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
    }
#else
    // ggml_graph defrag

    ggml_backend_sched_reset(lctx.sched);

    ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);

    llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
#endif

    //const int64_t t_end = ggml_time_us();

    //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
}

static void llama_kv_cache_update_internal(struct llama_context & lctx) {
    bool need_reserve = false;

    // apply K-shift if needed
    if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
        {
            ggml_backend_sched_reset(lctx.sched);

            ggml_cgraph * gf = llama_build_graph_k_shift(lctx);

            ggml_backend_sched_alloc_graph(lctx.sched, gf);

            llama_set_k_shift(lctx);

            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);

            need_reserve = true;
        }

        {
            auto & kv_self = lctx.kv_self;

            kv_self.has_shift = false;

            for (uint32_t i = 0; i < kv_self.size; ++i) {
                kv_self.cells[i].delta = 0;
            }
        }
    }

    if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
        {
            ggml_backend_sched_reset(lctx.sched);

            ggml_cgraph * gf = llama_build_graph_s_copy(lctx);

            ggml_backend_sched_alloc_graph(lctx.sched, gf);

            llama_set_s_copy(lctx);

            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);

            need_reserve = true;
        }

        {
            auto & kv_self = lctx.kv_self;

            kv_self.do_copy = false;

            for (uint32_t i = 0; i < kv_self.size; ++i) {
                kv_self.cells[i].src = i;
            }
        }
    }

    // defragment the KV cache if needed
    if (lctx.kv_self.do_defrag) {
        llama_kv_cache_defrag_internal(lctx);

        need_reserve = true;

        lctx.kv_self.do_defrag = false;
    }

    // reserve a worst case graph again
    if (need_reserve) {
        // TODO: extract to a function
        // build worst-case graph
        int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
        int n_past = lctx.cparams.n_ctx - n_tokens;
        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
        ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);

        // initialize scheduler with the worst-case graph
        ggml_backend_sched_reset(lctx.sched);
        if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
        }
    }
}

//
// tokenizer
//

static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
    return vocab.type;
}

static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
}

static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
}

static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
}

static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
}

static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
}

static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
    GGML_ASSERT(llama_is_byte_token(vocab, id));
    const auto& token_data = vocab.id_to_token.at(id);
    switch (llama_vocab_get_type(vocab)) {
        case LLAMA_VOCAB_TYPE_SPM: {
            auto buf = token_data.text.substr(3, 2);
            return strtol(buf.c_str(), NULL, 16);
        }
        case LLAMA_VOCAB_TYPE_BPE: {
            GGML_ASSERT(false);
            return unicode_utf8_to_byte(token_data.text);
        }
        case LLAMA_VOCAB_TYPE_WPM: {
            GGML_ASSERT(false);
        }
        default:
            GGML_ASSERT(false);
    }
}

static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
    static const char * hex = "0123456789ABCDEF";
    switch (llama_vocab_get_type(vocab)) {
        case LLAMA_VOCAB_TYPE_SPM: {
            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
            auto token = vocab.token_to_id.find(buf);
            if (token != vocab.token_to_id.end()) {
                return (*token).second;
            }
            // Try to fall back to just the byte as a string
            const char buf2[2] = { (char)ch, 0 };
            return vocab.token_to_id.at(buf2);
        }
        case LLAMA_VOCAB_TYPE_WPM:
        case LLAMA_VOCAB_TYPE_BPE: {
            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
        }
        default:
            GGML_ASSERT(false);
    }
}

static void llama_escape_whitespace(std::string & text) {
    replace_all(text, " ", "\xe2\x96\x81");
}

static void llama_unescape_whitespace(std::string & word) {
    replace_all(word, "\xe2\x96\x81", " ");
}

struct llm_symbol {
    using index = int;
    index prev;
    index next;
    const char * text;
    size_t n;
};

static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");

// SPM tokenizer
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4

struct llm_bigram_spm {
    struct comparator {
        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
        }
    };
    using queue_storage = std::vector<llm_bigram_spm>;
    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
    llm_symbol::index left;
    llm_symbol::index right;
    float score;
    size_t size;
};

struct llm_tokenizer_spm {
    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        // split string into utf8 chars
        int index = 0;
        size_t offs = 0;
        while (offs < text.size()) {
            llm_symbol sym;
            size_t len = utf8_len(text[offs]);
            sym.text = text.c_str() + offs;
            sym.n = std::min(len, text.size() - offs);
            offs += sym.n;
            sym.prev = index - 1;
            sym.next = offs == text.size() ? -1 : index + 1;
            index++;
            symbols.emplace_back(sym);
        }

        // seed the work queue with all possible 2-character tokens.
        for (size_t i = 1; i < symbols.size(); ++i) {
            try_add_bigram(i - 1, i);
        }

        // keep substituting the highest frequency pairs for as long as we can.
        while (!work_queue.empty()) {
            auto bigram = work_queue.top();
            work_queue.pop();

            auto & left_sym = symbols[bigram.left];
            auto & right_sym = symbols[bigram.right];

            // if one of the symbols already got merged, skip it.
            if (left_sym.n == 0 || right_sym.n == 0 ||
                left_sym.n + right_sym.n != bigram.size) {
                continue;
            }

            // merge the right sym into the left one
            left_sym.n += right_sym.n;
            right_sym.n = 0;

            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);

            // remove the right sym from the chain
            left_sym.next = right_sym.next;
            if (right_sym.next >= 0) {
                symbols[right_sym.next].prev = bigram.left;
            }

            // find more substitutions
            try_add_bigram(left_sym.prev, bigram.left);
            try_add_bigram(bigram.left, left_sym.next);
        }

        for (int i = 0; i != -1; i = symbols[i].next) {
            auto & symbol = symbols[i];
            resegment(symbol, output);
        }
    }

private:
    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
        auto text = std::string(symbol.text, symbol.n);
        auto token = vocab.token_to_id.find(text);

        // Do we need to support is_unused?
        if (token != vocab.token_to_id.end()) {
            output.push_back((*token).second);
            return;
        }

        const auto p = rev_merge.find(text);

        if (p == rev_merge.end()) {
            // output any symbols that did not form tokens as bytes.
            output.reserve(output.size() + symbol.n);
            for (int j = 0; j < (int)symbol.n; ++j) {
                llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
                output.push_back(token_id);
            }
            return;
        }

        resegment(symbols[p->second.first],  output);
        resegment(symbols[p->second.second], output);
    }

    void try_add_bigram(int left, int right) {
        if (left == -1 || right == -1) {
            return;
        }

        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
        auto token = vocab.token_to_id.find(text);

        if (token == vocab.token_to_id.end()) {
            return;
        }

        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
            return;
        }

        const auto & tok_data = vocab.id_to_token[(*token).second];

        llm_bigram_spm bigram;
        bigram.left  = left;
        bigram.right = right;
        bigram.score = tok_data.score;
        bigram.size  = text.size();

        work_queue.push(bigram);

        // Do we need to support is_unused?
        rev_merge[text] = std::make_pair(left, right);
    }

    const llama_vocab & vocab;

    std::vector<llm_symbol> symbols;
    llm_bigram_spm::queue work_queue;

    std::map<std::string, std::pair<int, int>> rev_merge;
};

// BPE tokenizer
// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
// tried to simplify unicode stuff, so most likely does not work 100% correctly!

// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused

struct llm_bigram_bpe {
    struct comparator {
        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
        }
    };

    using queue_storage = std::vector<llm_bigram_bpe>;
    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
    llm_symbol::index left;
    llm_symbol::index right;
    std::string text;
    int rank;
    size_t size;
};

struct llm_tokenizer_bpe {
    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        int final_prev_index = -1;
        auto word_collection = bpe_gpt2_preprocess(text);

        symbols_final.clear();

        for (auto & word : word_collection) {
            work_queue = llm_bigram_bpe::queue();
            symbols.clear();

            int index = 0;
            size_t offset = 0;

            while (offset < word.size()) {
                llm_symbol sym;
                size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
                sym.text = word.c_str() + offset;
                sym.n = char_len;
                offset += sym.n;
                sym.prev = index - 1;
                sym.next = offset == word.size() ? -1 : index + 1;
                index++;
                symbols.emplace_back(sym);
            }
            for (size_t i = 1; i < symbols.size(); ++i) {
                add_new_bigram(i - 1, i);
            }

            // build token(s)
            while (!work_queue.empty()) {
                auto bigram = work_queue.top();
                work_queue.pop();

                auto & left_symbol = symbols[bigram.left];
                auto & right_symbol = symbols[bigram.right];

                if (left_symbol.n == 0 || right_symbol.n == 0) {
                    continue;
                }
                std::string left_token = std::string(left_symbol.text, left_symbol.n);
                std::string right_token = std::string(right_symbol.text, right_symbol.n);
                if (left_token + right_token != bigram.text) {
                    continue;  // Skip this bigram if it's outdated
                }

                // merge the right sym into the left one
                left_symbol.n += right_symbol.n;
                right_symbol.n = 0;

                // remove the right sym from the chain
                left_symbol.next = right_symbol.next;
                if (right_symbol.next >= 0) {
                    symbols[right_symbol.next].prev = bigram.left;
                }

                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
            }

            // add the fnished tokens to the final list keeping correct order for next and prev
            for (auto & sym : symbols) {
                if (sym.n > 0) {
                    sym.prev = final_prev_index;
                    sym.next = -1;
                    if (final_prev_index != -1) {
                        symbols_final[final_prev_index].next = symbols_final.size();
                    }
                    symbols_final.emplace_back(sym);
                    final_prev_index = symbols_final.size() - 1;
                }
            }
        }

        symbols = symbols_final;

        if (!symbols.empty()) {
            for (int i = 0; i != -1; i = symbols[i].next) {
                auto & symbol = symbols[i];
                if (symbol.n == 0) {
                    continue;
                }

                const std::string str = std::string(symbol.text, symbol.n);
                const auto token = vocab.token_to_id.find(str);

                if (token == vocab.token_to_id.end()) {
                    for (auto j = str.begin(); j != str.end(); ++j) {
                        std::string byte_str(1, *j);
                        auto token_multibyte = vocab.token_to_id.find(byte_str);
                        if (token_multibyte == vocab.token_to_id.end()) {
                            throw std::runtime_error("ERROR: byte not found in vocab");
                        }
                        output.push_back((*token_multibyte).second);
                    }
                } else {
                    output.push_back((*token).second);
                }
            }
        }
    }

private:
    void add_new_bigram(int left, int right) {
        if (left == -1 || right == -1) {
            return;
        }

        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
        std::string right_token = std::string(symbols[right].text, symbols[right].n);

        int rank_found = -1;

        rank_found = vocab.find_bpe_rank(left_token, right_token);

        if (rank_found < 0) {
            return;
        }

        llm_bigram_bpe bigram;

        bigram.left  = left;
        bigram.right = right;
        bigram.text  = left_token + right_token;
        bigram.size  = left_token.size() + right_token.size();
        bigram.rank  = rank_found;

        work_queue.push(bigram);
    }

    std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
        std::vector<std::string> bpe_words;
        std::vector<std::string> bpe_encoded_words;

        std::string token = "";
        // GPT2 system regex:  's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
        bool collecting_numeric = false;
        bool collecting_letter = false;
        bool collecting_special = false;
        bool collecting_whitespace_lookahead = false;
        bool collecting = false;

        std::vector<std::string> text_utf;
        text_utf.reserve(text.size());
        bpe_words.reserve(text.size());
        bpe_encoded_words.reserve(text.size());

        const auto cpts = unicode_cpts_from_utf8(text);
        for (size_t i = 0; i < cpts.size(); ++i)
            text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i]));

        for (int i = 0; i < (int)text_utf.size(); i++) {
            const std::string & utf_char = text_utf[i];
            bool split_condition = false;
            int bytes_remain = text_utf.size() - i;
            // forward backward lookups
            const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
            const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";

            // handling contractions
            if (!split_condition && bytes_remain >= 2) {
                // 's|'t|'m|'d
                if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
                    split_condition = true;
                }
                if (split_condition) {
                    if (token.size()) {
                        bpe_words.emplace_back(token); // push previous content as token
                    }
                    token = utf_char + utf_char_next;
                    bpe_words.emplace_back(token);
                    token = "";
                    i++;
                    continue;
                }
            }
            if (!split_condition && bytes_remain >= 3) {
                // 're|'ve|'ll
                if (utf_char == "\'" && (
                    (utf_char_next == "r" && utf_char_next_next == "e") ||
                    (utf_char_next == "v" && utf_char_next_next == "e") ||
                    (utf_char_next == "l" && utf_char_next_next == "l"))
                    ) {
                    split_condition = true;
                }
                if (split_condition) {
                    // current token + next token can be defined
                    if (token.size()) {
                        bpe_words.emplace_back(token); // push previous content as token
                    }
                    token = utf_char + utf_char_next + utf_char_next_next;
                    bpe_words.emplace_back(token); // the contraction
                    token = "";
                    i += 2;
                    continue;
                }
            }

            if (!split_condition && !collecting) {
                if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
                    collecting_letter = true;
                    collecting = true;
                }
                else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
                    collecting_numeric = true;
                    collecting = true;
                }
                else if (
                    ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
                    (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
                    ) {
                    collecting_special = true;
                    collecting = true;
                }
                else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
                    collecting_whitespace_lookahead = true;
                    collecting = true;
                }
                else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
                    split_condition = true;
                }
            }
            else if (!split_condition && collecting) {
                if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) {
                    split_condition = true;
                }
                else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
                    split_condition = true;
                }
                else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
                    split_condition = true;
                }
                else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
                    split_condition = true;
                }
            }

            if (utf_char_next == "") {
                split_condition = true; // final
                token += utf_char;
            }

            if (split_condition) {
                if (token.size()) {
                    bpe_words.emplace_back(token);
                }
                token = utf_char;
                collecting = false;
                collecting_letter = false;
                collecting_numeric = false;
                collecting_special = false;
                collecting_whitespace_lookahead = false;
            }
            else {
                token += utf_char;
            }
        }

        for (std::string & word : bpe_words) {
            std::string encoded_token = "";
            for (char & c : word) {
                encoded_token += unicode_byte_to_utf8(c);
            }
            bpe_encoded_words.emplace_back(encoded_token);
        }

        return bpe_encoded_words;
    }

    const llama_vocab & vocab;

    std::vector<llm_symbol> symbols;
    std::vector<llm_symbol> symbols_final;

    llm_bigram_bpe::queue work_queue;
};

struct llm_tokenizer_wpm {
    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        auto * token_map = &vocab.token_to_id;

        // normalize and split by whitespace
        std::vector<std::string> words = preprocess(text);

        // bos token prepended already

        // find the longest tokens that form the words
        for (const std::string &word : words) {
            // skip empty words
            if (word.size() == 0) {
                continue;
            }

            // prepend phantom space
            std::string word1 = "\xe2\x96\x81" + word;
            int n = word1.size();

            // we're at the start of a new word
            int i = 0;
            bool match_any = false;

            // move through character position in word
            while (i < n) {
                // loop through possible match length
                bool match = false;
                for (int j = n; j > i; j--) {
                    auto it = token_map->find(word1.substr(i, j - i));
                    if (it != token_map->end()) {
                        output.push_back(it->second);
                        match = true;
                        match_any = true;
                        i = j;
                        break;
                    }
                }

                // must be an unknown character
                if (!match) {
                    i++;
                }
            }

            // we didn't find any matches for this word
            if (!match_any) {
                output.push_back(vocab.special_unk_id);
            }
        }

        // append eos token
        output.push_back(vocab.special_eos_id);
    }

    std::vector<std::string> preprocess(const std::string & text) {
        std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));

        // strip accents, strip control, uniformize whitespace,
        // to lowercase, pad chinese characters, pad punctuation
        std::string new_str = "";
        for (uint32_t code : cpts_nfd) {
            int type = unicode_cpt_type(code);
            if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) {
                continue;
            }
            code = to_lower(code);
            if (type == CODEPOINT_TYPE_WHITESPACE) {
                code = ' ';
            }
            std::string s = unicode_cpt_to_utf8(code);
            if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) {
                new_str += " ";
                new_str += s;
                new_str += " ";
            } else {
                new_str += s;
            }
        }

        // split by whitespace
        uint64_t l = 0;
        uint64_t r = 0;
        std::vector<std::string> words;
        while (r < new_str.size()) {
            // if is whitespace
            if (isspace(new_str[r])) {
                if (r > l) words.push_back(new_str.substr(l, (r - l)));
                l = r + 1;
                r = l;
            } else {
                r += 1;
            }
        }
        if (r > l) {
            words.push_back(new_str.substr(l, (r - l)));
        }
        return words;
    }

    uint32_t to_lower(uint32_t code) {
        static const std::locale locale("en_US.UTF-8");
#if defined(_WIN32)
        if (code > 0xFFFF) {
            return code;
        }
#endif
        return std::tolower(wchar_t(code), locale);
    }

    bool is_ascii_punct(uint32_t code) {
        return code < 256 && ispunct(code);
    }

    bool is_chinese_char(uint32_t cpt) {
        if ((cpt >= 0x4E00  && cpt <= 0x9FFF)  ||
            (cpt >= 0x3400  && cpt <= 0x4DBF)  ||
            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
            (cpt >= 0xF900  && cpt <= 0xFAFF)  ||
            (cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
            (cpt >= 0x3000  && cpt <= 0x303F)  ||
            (cpt >= 0xFF00  && cpt <= 0xFFEF)) {
            return true; // NOLINT
        }
        return false;
    }

    const llama_vocab & vocab;
};

typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
} FRAGMENT_BUFFER_VARIANT_TYPE;

struct fragment_buffer_variant {
    fragment_buffer_variant(llama_vocab::id _token)
    :
        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
        token(_token),
        raw_text(_dummy),
        offset(0),
        length(0) {}

    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
    :
        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
        token((llama_vocab::id) - 1),
        raw_text(_raw_text),
        offset(_offset),
        length(_length){
            GGML_ASSERT(_offset >= 0);
            GGML_ASSERT(_length >= 1);
            GGML_ASSERT(offset + length <= raw_text.length());
        }

    const FRAGMENT_BUFFER_VARIANT_TYPE type;
    const llama_vocab::id token;
    const std::string _dummy;
    const std::string & raw_text;
    const uint64_t offset;
    const uint64_t length;
};

// #define PRETOKENIZERDEBUG

static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
    // for each special token
    for (const auto & st: vocab.special_tokens_cache) {
        const auto & special_token = st.first;
        const auto & special_id    = st.second;

        // for each text fragment
        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
        while (it != buffer.end()) {
            auto & fragment = (*it);

            // if a fragment is text ( not yet processed )
            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                auto * raw_text = &(fragment.raw_text);

                auto raw_text_base_offset = fragment.offset;
                auto raw_text_base_length = fragment.length;

                // loop over the text
                while (true) {
                    // find the first occurrence of a given special token in this fragment
                    //  passing offset argument only limit the "search area" but match coordinates
                    //  are still relative to the source full raw_text
                    auto match = raw_text->find(special_token, raw_text_base_offset);

                    // no occurrences found, stop processing this fragment for a given special token
                    if (match == std::string::npos) break;

                    // check if match is within bounds of offset <-> length
                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;

#ifdef PRETOKENIZERDEBUG
                    LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
#endif
                    auto source = std::distance(buffer.begin(), it);

                    // if match is further than base offset
                    //  then we have some text to the left of it
                    if (match > raw_text_base_offset) {
                        // left
                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
                        const int64_t left_reminder_length = match - raw_text_base_offset;
                        buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);

#ifdef PRETOKENIZERDEBUG
                        LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
#endif
                        it++;
                    }

                    // special token
                    buffer.emplace_after(it, special_id);
                    it++;

                    // right
                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
                        const int64_t right_reminder_offset = match + special_token.length();
                        const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
                        buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);

#ifdef PRETOKENIZERDEBUG
                        LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
#endif

                        it++;

                        if (source == 0) {
                            buffer.erase_after(buffer.before_begin());
                        } else {
                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
                        }

                        // repeat for the right side
                        raw_text_base_offset = right_reminder_offset;
                        raw_text_base_length = right_reminder_length;

#ifdef PRETOKENIZERDEBUG
                        LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
#endif
                    } else {
                        if (source == 0) {
                            buffer.erase_after(buffer.before_begin());
                        } else {
                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
                        }
                        break;
                    }
                }
            }
            it++;
        }
    }
}

static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
    std::vector<llama_vocab::id> output;

    // OG tokenizer behavior:
    //
    // tokenizer.encode('', add_bos=True)  returns [1]
    // tokenizer.encode('', add_bos=False) returns []

    if (bos && vocab.special_bos_id != -1) {
        output.push_back(vocab.special_bos_id);
    }

    if (raw_text.empty()) {
        return output;
    }

    std::forward_list<fragment_buffer_variant> fragment_buffer;
    fragment_buffer.emplace_front(raw_text, 0, raw_text.length());

    if (special) tokenizer_st_partition(vocab, fragment_buffer);

    switch (vocab.type) {
        case LLAMA_VOCAB_TYPE_SPM:
            {
                for (const auto & fragment : fragment_buffer) {
                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                        // without adding this leading whitespace, we do not get the same results as the original tokenizer

                        // TODO: It's likely possible to get rid of this string copy entirely
                        //  by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
                        //  and passing 'add space prefix' as bool argument
                        //
                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
                        if (&fragment == &fragment_buffer.front()) {
                            if (vocab.add_space_prefix) {
                                raw_text = " " + raw_text; // prefix with space if the first token is not special
                            }
                        }

#ifdef PRETOKENIZERDEBUG
                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
                        llm_tokenizer_spm tokenizer(vocab);
                        llama_escape_whitespace(raw_text);
                        tokenizer.tokenize(raw_text, output);
                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                        output.push_back(fragment.token);
                    }
                }
            } break;
        case LLAMA_VOCAB_TYPE_BPE:
            {
                for (const auto & fragment : fragment_buffer) {
                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);

#ifdef PRETOKENIZERDEBUG
                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
                        llm_tokenizer_bpe tokenizer(vocab);
                        tokenizer.tokenize(raw_text, output);
                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                        output.push_back(fragment.token);
                    }
                }
            } break;
        case LLAMA_VOCAB_TYPE_WPM:
            {
                for (const auto & fragment : fragment_buffer) {
                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);

#ifdef PRETOKENIZERDEBUG
                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
                        llm_tokenizer_wpm tokenizer(vocab);
                        tokenizer.tokenize(raw_text, output);
                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                        output.push_back(fragment.token);
                    }
                }
            } break;
    }

    return output;
}

//
// grammar - internal
//

struct llama_partial_utf8 {
    uint32_t value;    // bit value so far (unshifted)
    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
};

struct llama_grammar {
    const std::vector<std::vector<llama_grammar_element>>   rules;
    std::vector<std::vector<const llama_grammar_element *>> stacks;

    // buffer for partially generated UTF-8 sequence from accepted tokens
    llama_partial_utf8                                      partial_utf8;
};

struct llama_grammar_candidate {
    size_t               index;
    const uint32_t     * code_points;
    llama_partial_utf8   partial_utf8;
};

// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
        const std::string & src,
        llama_partial_utf8   partial_start) {
    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
    const char          * pos      = src.c_str();
    std::vector<uint32_t> code_points;
    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
    code_points.reserve(src.size() + 1);
    uint32_t              value    = partial_start.value;
    int                   n_remain = partial_start.n_remain;

    // continue previous decode, if applicable
    while (*pos != 0 && n_remain > 0) {
        uint8_t next_byte = static_cast<uint8_t>(*pos);
        if ((next_byte >> 6) != 2) {
            // invalid sequence, abort
            code_points.push_back(0);
            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
        }
        value = (value << 6) + (next_byte & 0x3F);
        ++pos;
        --n_remain;
    }

    if (partial_start.n_remain > 0 && n_remain == 0) {
        code_points.push_back(value);
    }

    // decode any subsequent utf-8 sequences, which may end in an incomplete one
    while (*pos != 0) {
        uint8_t  first_byte = static_cast<uint8_t>(*pos);
        uint8_t  highbits   = first_byte >> 4;
                 n_remain   = lookup[highbits] - 1;

        if (n_remain < 0) {
            // invalid sequence, abort
            code_points.clear();
            code_points.push_back(0);
            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
        }

        uint8_t  mask       = (1 << (7 - n_remain)) - 1;
                 value      = first_byte & mask;
        ++pos;
        while (*pos != 0 && n_remain > 0) {
            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
            ++pos;
            --n_remain;
        }
        if (n_remain == 0) {
            code_points.push_back(value);
        }
    }
    code_points.push_back(0);

    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
}

// returns true iff pos points to the end of one of the definitions of a rule
static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
    switch (pos->type) {
        case LLAMA_GRETYPE_END: return true;  // NOLINT
        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
        default:                return false;
    }
}

// returns true iff chr satisfies the char range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
        const llama_grammar_element * pos,
        const uint32_t                chr) {

    bool found            = false;
    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;

    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT

    do {
        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
            // inclusive range, e.g. [a-z]
            found = found || (pos->value <= chr && chr <= pos[1].value);
            pos += 2;
        } else {
            // exact char match, e.g. [a] or "a"
            found = found || pos->value == chr;
            pos += 1;
        }
    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);

    return std::make_pair(found == is_positive_char, pos);
}

// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
// range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static bool llama_grammar_match_partial_char(
        const llama_grammar_element * pos,
        const llama_partial_utf8      partial_utf8) {

    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);

    uint32_t partial_value = partial_utf8.value;
    int      n_remain      = partial_utf8.n_remain;

    // invalid sequence or 7-bit char split across 2 bytes (overlong)
    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
        return false;
    }

    // range of possible code points this partial UTF-8 sequence could complete to
    uint32_t low  = partial_value << (n_remain * 6);
    uint32_t high = low | ((1 << (n_remain * 6)) - 1);

    if (low == 0) {
        if (n_remain == 2) {
            low = 1 << 11;
        } else if (n_remain == 3) {
            low = 1 << 16;
        }
    }

    do {
        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
            // inclusive range, e.g. [a-z]
            if (pos->value <= high && low <= pos[1].value) {
                return is_positive_char;
            }
            pos += 2;
        } else {
            // exact char match, e.g. [a] or "a"
            if (low <= pos->value && pos->value <= high) {
                return is_positive_char;
            }
            pos += 1;
        }
    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);

    return !is_positive_char;
}


// transforms a grammar pushdown stack into N possible stacks, all ending
// at a character range (terminal element)
static void llama_grammar_advance_stack(
        const std::vector<std::vector<llama_grammar_element>>   & rules,
        const std::vector<const llama_grammar_element *>        & stack,
        std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {

    if (stack.empty()) {
        new_stacks.emplace_back(stack);
        return;
    }

    const llama_grammar_element * pos = stack.back();

    switch (pos->type) {
        case LLAMA_GRETYPE_RULE_REF: {
            const size_t                  rule_id = static_cast<size_t>(pos->value);
            const llama_grammar_element * subpos  = rules[rule_id].data();
            do {
                // init new stack without the top (pos)
                std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
                    // if this rule ref is followed by another element, add that to stack
                    new_stack.push_back(pos + 1);
                }
                if (!llama_grammar_is_end_of_sequence(subpos)) {
                    // if alternate is nonempty, add to stack
                    new_stack.push_back(subpos);
                }
                llama_grammar_advance_stack(rules, new_stack, new_stacks);
                while (!llama_grammar_is_end_of_sequence(subpos)) {
                    // scan to end of alternate def
                    subpos++;
                }
                if (subpos->type == LLAMA_GRETYPE_ALT) {
                    // there's another alternate def of this rule to process
                    subpos++;
                } else {
                    break;
                }
            } while (true);
            break;
        }
        case LLAMA_GRETYPE_CHAR:
        case LLAMA_GRETYPE_CHAR_NOT:
            new_stacks.emplace_back(stack);
            break;
        default:
            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
            // those
            GGML_ASSERT(false);
    }
}

// takes a set of possible pushdown stacks on a grammar, which are required to
// be positioned at a character range (see `llama_grammar_advance_stack`), and
// produces the N possible stacks if the given char is accepted at those
// positions
static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
        const std::vector<std::vector<llama_grammar_element>>         & rules,
        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
        const uint32_t                                                  chr) {

    std::vector<std::vector<const llama_grammar_element *>> new_stacks;

    for (const auto & stack : stacks) {
        if (stack.empty()) {
            continue;
        }

        auto match = llama_grammar_match_char(stack.back(), chr);
        if (match.first) {
            const llama_grammar_element * pos = match.second;

            // update top of stack to next element, if any
            std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
            if (!llama_grammar_is_end_of_sequence(pos)) {
                new_stack.push_back(pos);
            }
            llama_grammar_advance_stack(rules, new_stack, new_stacks);
        }
    }

    return new_stacks;
}

static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
        const std::vector<std::vector<llama_grammar_element>>         & rules,
        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
        const std::vector<llama_grammar_candidate>                    & candidates);

static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
        const std::vector<std::vector<llama_grammar_element>> & rules,
        const std::vector<const llama_grammar_element *>      & stack,
        const std::vector<llama_grammar_candidate>            & candidates) {

    std::vector<llama_grammar_candidate> rejects;

    if (stack.empty()) {
        for (const auto & tok : candidates) {
            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
                rejects.push_back(tok);
            }
        }
        return rejects;
    }

    const llama_grammar_element * stack_pos = stack.back();

    std::vector<llama_grammar_candidate> next_candidates;
    for (const auto & tok : candidates) {
        if (*tok.code_points == 0) {
            // reached end of full codepoints in token, reject iff it ended in a partial sequence
            // that cannot satisfy this position in grammar
            if (tok.partial_utf8.n_remain != 0 &&
                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
                rejects.push_back(tok);
            }
        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
        } else {
            rejects.push_back(tok);
        }
    }

    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;

    // update top of stack to next element, if any
    std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
        stack_after.push_back(stack_pos_after);
    }
    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
    llama_grammar_advance_stack(rules, stack_after, next_stacks);

    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
    for (const auto & tok : next_rejects) {
        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
    }

    return rejects;
}

static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
        const std::vector<std::vector<llama_grammar_element>>         & rules,
        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
        const std::vector<llama_grammar_candidate>                    & candidates) {
    GGML_ASSERT(!stacks.empty()); // REVIEW

    if (candidates.empty()) {
        return std::vector<llama_grammar_candidate>();
    }

    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);

    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
    }
    return rejects;
}

//
// grammar - external
//

struct llama_grammar * llama_grammar_init(
            const llama_grammar_element ** rules,
                                 size_t    n_rules,
                                 size_t    start_rule_index) {
    const llama_grammar_element * pos;

    // copy rule definitions into vectors
    std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
    for (size_t i = 0; i < n_rules; i++) {
        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
            vec_rules[i].push_back(*pos);
        }
        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
    }

    // loop over alternates of start rule to build initial stacks
    std::vector<std::vector<const llama_grammar_element *>> stacks;
    pos = vec_rules[start_rule_index].data();
    do {
        std::vector<const llama_grammar_element *> stack;
        if (!llama_grammar_is_end_of_sequence(pos)) {
            // if alternate is nonempty, add to stack
            stack.push_back(pos);
        }
        llama_grammar_advance_stack(vec_rules, stack, stacks);
        while (!llama_grammar_is_end_of_sequence(pos)) {
            // scan to end of alternate def
            pos++;
        }
        if (pos->type == LLAMA_GRETYPE_ALT) {
            // there's another alternate def of this rule to process
            pos++;
        } else {
            break;
        }
    } while (true);

    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
}

void llama_grammar_free(struct llama_grammar * grammar) {
    delete grammar;
}

struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };

    // redirect elements in stacks to point to new rules
    for (size_t is = 0; is < result->stacks.size(); is++) {
        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
                    }
                }
            }
        }
    }

    return result;
}

//
// sampling
//

void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
    if (seed == LLAMA_DEFAULT_SEED) {
        seed = time(NULL);
    }
    ctx->rng.seed(seed);
}

void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
    GGML_ASSERT(candidates->size > 0);

    const int64_t t_start_sample_us = ggml_time_us();

    // Sort the logits in descending order
    if (!candidates->sorted) {
        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
            return a.logit > b.logit;
        });
        candidates->sorted = true;
    }

    float max_l = candidates->data[0].logit;
    float cum_sum = 0.0f;
    for (size_t i = 0; i < candidates->size; ++i) {
        float p = expf(candidates->data[i].logit - max_l);
        candidates->data[i].p = p;
        cum_sum += p;
    }
    for (size_t i = 0; i < candidates->size; ++i) {
        candidates->data[i].p /= cum_sum;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
    // if (k >= (int32_t)candidates->size) {
    //     return;
    // }

    const int64_t t_start_sample_us = ggml_time_us();

    if (k <= 0) {
        k = candidates->size;
    }

    k = std::max(k, (int) min_keep);
    k = std::min(k, (int) candidates->size);

    // Sort scores in descending order
    if (!candidates->sorted) {
        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
            return a.logit > b.logit;
        };
        if (k <= 128) {
            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
        } else {
            constexpr int   nbuckets     = 128;
            constexpr float bucket_low   = -10.0f;
            constexpr float bucket_high  =  10.0f;
            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
            constexpr float bucker_inter = -bucket_low * bucket_scale;

            std::vector<int> bucket_idx(candidates->size);
            std::vector<int> histo(nbuckets, 0);

            for (int i = 0; i < (int)candidates->size; ++i) {
                const float val = candidates->data[i].logit;
                int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
                ib = std::max(0, std::min(nbuckets-1, ib));
                bucket_idx[i] = ib;
                ++histo[ib];
            }
            int nhave = 0;
            int ib = nbuckets - 1;
            for ( ; ib >= 0; --ib) {
                nhave += histo[ib];
                if (nhave >= k) break;
            }
            std::vector<llama_token_data> tmp_tokens(nhave);
            auto ptr = tmp_tokens.data();
            std::vector<llama_token_data*> bucket_ptrs;
            bucket_ptrs.reserve(nbuckets - ib);
            for (int j = nbuckets - 1; j >= ib; --j) {
                bucket_ptrs.push_back(ptr);
                ptr += histo[j];
            }
            for (int i = 0; i < (int)candidates->size; ++i) {
                int j = bucket_idx[i];
                if (j >= ib) {
                    *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
                }
            }

            ptr = tmp_tokens.data();
            int ndone = 0;
            for (int j = nbuckets-1; j > ib; --j) {
                std::sort(ptr, ptr + histo[j], comp);
                ptr += histo[j];
                ndone += histo[j];
            }
            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);

            std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));

        }
        candidates->sorted = true;
    }
    candidates->size = k;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
    if (p >= 1.0f) {
        return;
    }

    llama_sample_softmax(ctx, candidates);

    const int64_t t_start_sample_us = ggml_time_us();

    // Compute the cumulative probabilities
    float cum_sum = 0.0f;
    size_t last_idx = candidates->size;

    for (size_t i = 0; i < candidates->size; ++i) {
        cum_sum += candidates->data[i].p;

        // Check if the running sum is at least p or if we have kept at least min_keep tokens
        // we set the last index to i+1 to indicate that the current iterate should be included in the set
        if (cum_sum >= p && i + 1 >= min_keep) {
            last_idx = i + 1;
            break;
        }
    }

    // Resize the output vector to keep only the top-p tokens
    candidates->size = last_idx;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
    if (p <= 0.0f || !candidates->size) {
        return;
    }

    const int64_t t_start_sample_us = ggml_time_us();

    bool min_p_applied = false;

    // if the candidates aren't sorted, try the unsorted implementation first
    if (!candidates->sorted) {
        std::vector<llama_token_data> filtered_tokens;

        float max_logit = -FLT_MAX;
        for (size_t i = 0; i < candidates->size; ++i) {
            max_logit = std::max(max_logit, candidates->data[i].logit);
        }
        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max

        for (size_t i = 0; i < candidates->size; ++i) {
            if (candidates->data[i].logit >= min_logit) {
                filtered_tokens.push_back(candidates->data[i]);
            }
        }

        // if we have enough values the operation was a success
        if (filtered_tokens.size() >= min_keep) {
            memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
            candidates->size = filtered_tokens.size();
            min_p_applied = true;
        }
    }

    // if the candidates are sorted or the unsorted implementation failed, use this implementation
    if (!min_p_applied) {
        // Sort the logits in descending order
        if (!candidates->sorted) {
            std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
                return a.logit > b.logit;
            });
            candidates->sorted = true;
        }

        const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
        size_t i = 1; // first token always matches

        for (; i < candidates->size; ++i) {
            if (candidates->data[i].logit < min_logit && i >= min_keep) {
                break; // prob too small
            }
        }

        // Resize the output vector to keep only the matching tokens
        candidates->size = i;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
    if (z >= 1.0f || candidates->size <= 2) {
        return;
    }

    llama_sample_softmax(nullptr, candidates);
    const int64_t t_start_sample_us = ggml_time_us();

    // Compute the first and second derivatives
    std::vector<float> first_derivatives(candidates->size - 1);
    std::vector<float> second_derivatives(candidates->size - 2);

    for (size_t i = 0; i < first_derivatives.size(); ++i) {
        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
    }
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
    }

    // Calculate absolute value of second derivatives
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        second_derivatives[i] = std::abs(second_derivatives[i]);
    }

    // Normalize the second derivatives
    {
        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);

        if (second_derivatives_sum > 1e-6f) {
            for (float & value : second_derivatives) {
                value /= second_derivatives_sum;
            }
        } else {
            for (float & value : second_derivatives) {
                value = 1.0f / second_derivatives.size();
            }
        }
    }

    float cum_sum = 0.0f;
    size_t last_idx = candidates->size;
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        cum_sum += second_derivatives[i];

        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
        if (cum_sum > z && i >= min_keep) {
            last_idx = i;
            break;
        }
    }

    // Resize the output vector to keep only the tokens above the tail location
    candidates->size = last_idx;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
    // Reference implementation:
    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
    if (p >= 1.0f) {
        return;
    }

    // Compute the softmax of logits and calculate entropy
    llama_sample_softmax(nullptr, candidates);

    const int64_t t_start_sample_us = ggml_time_us();

    float entropy = 0.0f;
    for (size_t i = 0; i < candidates->size; ++i) {
        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
    }

    // Compute the absolute difference between negative log probability and entropy for each candidate
    std::vector<float> shifted_scores;
    for (size_t i = 0; i < candidates->size; ++i) {
        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
        shifted_scores.push_back(shifted_score);
    }

    // Sort tokens based on the shifted_scores and their corresponding indices
    std::vector<size_t> indices(candidates->size);
    std::iota(indices.begin(), indices.end(), 0);

    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
        return shifted_scores[a] < shifted_scores[b];
    });

    // Compute the cumulative probabilities
    float cum_sum = 0.0f;
    size_t last_idx = indices.size();

    for (size_t i = 0; i < indices.size(); ++i) {
        size_t idx = indices[i];
        cum_sum += candidates->data[idx].p;

        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
        if (cum_sum > p && i >= min_keep - 1) {
            last_idx = i + 1;
            break;
        }
    }

    // Resize the output vector to keep only the locally typical tokens
    std::vector<llama_token_data> new_candidates;
    for (size_t i = 0; i < last_idx; ++i) {
        size_t idx = indices[i];
        new_candidates.push_back(candidates->data[idx]);
    }

    // Replace the data in candidates with the new_candidates data
    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
    candidates->size = new_candidates.size();
    candidates->sorted = false;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
    const int64_t t_start_sample_us = ggml_time_us();

    // no need to do anything if there is only one (or zero) candidates
    if(candidates_p->size <= 1) {
        return;
    }

    // Calculate maximum possible entropy
    float max_entropy = -logf(1.0f / candidates_p->size);

    llama_sample_softmax(nullptr, candidates_p);

    // Calculate entropy of the softmax probabilities
    float entropy = 0.0f;
    for (size_t i = 0; i < candidates_p->size; ++i) {
        float prob = candidates_p->data[i].p;
        if (prob > 0.0f) { // Ensure no log(0)
            entropy -= prob * logf(prob);
        }
    }

    // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above)
    float normalized_entropy = entropy / max_entropy;

    // Map the normalized entropy to the desired temperature range using the power function
    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);

#ifdef DEBUG
    LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
    LLAMA_LOG_INFO("Entropy: %f\n", entropy);
    LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
    LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
    LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
    LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
#endif

    // Apply the dynamically calculated temperature scaling
    for (size_t i = 0; i < candidates_p->size; ++i) {
        candidates_p->data[i].logit /= dyn_temp;
    }

    // Re-compute softmax probabilities after scaling logits with dynamic temperature
    double max_l_double = candidates_p->data[0].logit;
    double cum_sum_double = 0.0;
    for (size_t i = 0; i < candidates_p->size; ++i) {
        double p = exp(candidates_p->data[i].logit - max_l_double);
        candidates_p->data[i].p = p; // Store the scaled probability
        cum_sum_double += p;
    }
    for (size_t i = 0; i < candidates_p->size; ++i) {
        candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
    }

#ifdef DEBUG
    // Print the updated top 25 probabilities after temperature scaling
    LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
    for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) {
        LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f);
    }
#endif

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
    const int64_t t_start_sample_us = ggml_time_us();

    for (size_t i = 0; i < candidates_p->size; ++i) {
        candidates_p->data[i].logit /= temp;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_repetition_penalties(
            struct llama_context * ctx,
          llama_token_data_array * candidates,
               const llama_token * last_tokens,
                          size_t   penalty_last_n,
                           float   penalty_repeat,
                           float   penalty_freq,
                           float   penalty_present) {
    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
        return;
    }

    const int64_t t_start_sample_us = ggml_time_us();

    // Create a frequency map to count occurrences of each token in last_tokens
    std::unordered_map<llama_token, int> token_count;
    for (size_t i = 0; i < penalty_last_n; ++i) {
        token_count[last_tokens[i]]++;
    }

    // Apply frequency and presence penalties to the candidates
    for (size_t i = 0; i < candidates->size; ++i) {
        const auto token_iter = token_count.find(candidates->data[i].id);
        if (token_iter == token_count.end()) {
            continue;
        }

        const int count = token_iter->second;

        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
        if (candidates->data[i].logit <= 0) {
            candidates->data[i].logit *= penalty_repeat;
        } else {
            candidates->data[i].logit /= penalty_repeat;
        }

        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
    }

    candidates->sorted = false;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
    GGML_ASSERT(ctx);
    const int64_t t_start_sample_us = ggml_time_us();

    bool allow_eos = false;
    for (const auto & stack : grammar->stacks) {
        if (stack.empty()) {
            allow_eos = true;
            break;
        }
    }

    const llama_token eos = llama_token_eos(&ctx->model);

    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
    candidates_decoded.reserve(candidates->size);
    std::vector<llama_grammar_candidate>                              candidates_grammar;
    candidates_grammar.reserve(candidates->size);

    for (size_t i = 0; i < candidates->size; ++i) {
        const llama_token id    = candidates->data[i].id;
        const std::string piece = llama_token_to_piece(ctx, id);
        if (id == eos) {
            if (!allow_eos) {
                candidates->data[i].logit = -INFINITY;
            }
        } else if (piece.empty() || piece[0] == 0) {
            candidates->data[i].logit = -INFINITY;
        } else {
            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
        }
    }

    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
    for (const auto & reject : rejects) {
        candidates->data[reject.index].logit = -INFINITY;
    }

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}

static void llama_log_softmax(float * array, size_t size) {
    float max_l = *std::max_element(array, array + size);
    float sum = 0.f;
    for (size_t i = 0; i < size; ++i) {
        float p = expf(array[i] - max_l);
        sum += p;
        array[i] = p;
    }

    for (size_t i = 0; i < size; ++i) {
        array[i] = logf(array[i] / sum);
    }
}

void llama_sample_apply_guidance(
          struct llama_context * ctx,
                         float * logits,
                         float * logits_guidance,
                         float   scale) {
    GGML_ASSERT(ctx);

    const auto t_start_sample_us = ggml_time_us();
    const auto n_vocab = llama_n_vocab(llama_get_model(ctx));

    llama_log_softmax(logits, n_vocab);
    llama_log_softmax(logits_guidance, n_vocab);

    for (int i = 0; i < n_vocab; ++i) {
              auto & l = logits[i];
        const auto & g = logits_guidance[i];

        l = scale * (l - g) + g;
    }

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}

llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
    GGML_ASSERT(ctx);

    auto N = float(llama_n_vocab(llama_get_model(ctx)));
    int64_t t_start_sample_us;
    t_start_sample_us = ggml_time_us();

    llama_sample_softmax(nullptr, candidates);

    // Estimate s_hat using the most probable m tokens
    float s_hat = 0.0;
    float sum_ti_bi = 0.0;
    float sum_ti_sq = 0.0;
    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
        float t_i = logf(float(i + 2) / float(i + 1));
        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
        sum_ti_bi += t_i * b_i;
        sum_ti_sq += t_i * t_i;
    }
    s_hat = sum_ti_bi / sum_ti_sq;

    // Compute k from the estimated s_hat and target surprise value
    float epsilon_hat = s_hat - 1;
    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);

    // Sample the next word X using top-k sampling
    llama_sample_top_k(nullptr, candidates, int(k), 1);
    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
    llama_token X = llama_sample_token(ctx, candidates);
    t_start_sample_us = ggml_time_us();

    // Compute error as the difference between observed surprise and target surprise value
    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return candidate.id == X;
    }));
    float observed_surprise = -log2f(candidates->data[X_idx].p);
    float e = observed_surprise - tau;

    // Update mu using the learning rate and error
    *mu = *mu - eta * e;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
    return X;
}

llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
    int64_t t_start_sample_us;
    t_start_sample_us = ggml_time_us();

    llama_sample_softmax(ctx, candidates);

    // Truncate the words with surprise values greater than mu
    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return -log2f(candidate.p) > *mu;
    }));

    if (candidates->size == 0) {
        candidates->size = 1;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }

    // Normalize the probabilities of the remaining words
    llama_sample_softmax(ctx, candidates);

    // Sample the next word X from the remaining words
    llama_token X = llama_sample_token(ctx, candidates);
    t_start_sample_us = ggml_time_us();

    // Compute error as the difference between observed surprise and target surprise value
    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return candidate.id == X;
    }));
    float observed_surprise = -log2f(candidates->data[X_idx].p);
    float e = observed_surprise - tau;

    // Update mu using the learning rate and error
    *mu = *mu - eta * e;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
    return X;
}

llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
    const int64_t t_start_sample_us = ggml_time_us();

    // Find max element
    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
        return a.logit < b.logit;
    });

    llama_token result = max_iter->id;
    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
        ctx->n_sample++;
    }
    return result;
}

llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
    GGML_ASSERT(ctx);

    const int64_t t_start_sample_us = ggml_time_us();
    llama_sample_softmax(nullptr, candidates);

    std::vector<float> probs;
    probs.reserve(candidates->size);
    for (size_t i = 0; i < candidates->size; ++i) {
        probs.push_back(candidates->data[i].p);
    }

    std::discrete_distribution<> dist(probs.begin(), probs.end());
    auto & rng = ctx->rng;
    int idx = dist(rng);

    llama_token result = candidates->data[idx].id;

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    ctx->n_sample++;
    return result;
}

void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
    const int64_t t_start_sample_us = ggml_time_us();

    if (token == llama_token_eos(&ctx->model)) {
        for (const auto & stack : grammar->stacks) {
            if (stack.empty()) {
                return;
            }
        }
        GGML_ASSERT(false);
    }

    const std::string piece = llama_token_to_piece(ctx, token);

    // Note terminating 0 in decoded string
    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
    const auto & code_points = decoded.first;
    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
    }
    grammar->partial_utf8 = decoded.second;
    GGML_ASSERT(!grammar->stacks.empty());

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}

//
// Beam search
//

struct llama_beam {
    std::vector<llama_token> tokens;
    float p;  // Cumulative beam probability (renormalized relative to all beams)
    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
    // Sort beams by probability. In case of ties, prefer beams at eob.
    bool operator<(const llama_beam & rhs) const {
        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
    }
    // Shift off first n tokens and discard them.
    void shift_tokens(const size_t n) {
        if (n) {
            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
            tokens.resize(tokens.size() - n);
        }
    }
    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
};

// A struct for calculating logit-related info.
struct llama_logit_info {
    const float * const logits;
    const int n_vocab;
    const float max_l;
    const float normalizer;
    struct sum_exp {
        float max_l;
        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
    };
    llama_logit_info(llama_context * ctx)
      : logits(llama_get_logits(ctx))
      , n_vocab(llama_n_vocab(llama_get_model(ctx)))
      , max_l(*std::max_element(logits, logits + n_vocab))
      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
      { }
    llama_token_data get_token_data(const llama_token token_id) const {
        constexpr auto p = std::numeric_limits<float>::quiet_NaN();  // never used
        return {token_id, logits[token_id], p};
    }
    // Return top k token_data by logit.
    std::vector<llama_token_data> top_k(size_t k) {
        std::vector<llama_token_data> min_heap;  // min-heap by logit
        const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
        min_heap.reserve(k_min);
        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
            min_heap.push_back(get_token_data(token_id));
        }
        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
        std::make_heap(min_heap.begin(), min_heap.end(), comp);
        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
            if (min_heap.front().logit < logits[token_id]) {
                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
                min_heap.back().id = token_id;
                min_heap.back().logit = logits[token_id];
                std::push_heap(min_heap.begin(), min_heap.end(), comp);
            }
        }
        return min_heap;
    }
    float probability_from_logit(float logit) const {
        return normalizer * std::exp(logit - max_l);
    }
};

struct llama_beam_search_data {
    llama_context * ctx;
    size_t n_beams;
    int n_past;
    int n_predict;
    std::vector<llama_beam> beams;
    std::vector<llama_beam> next_beams;

    // Re-calculated on each loop iteration
    size_t common_prefix_length;

    // Used to communicate to/from callback on beams state.
    std::vector<llama_beam_view> beam_views;

    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
      : ctx(ctx)
      , n_beams(n_beams)
      , n_past(n_past)
      , n_predict(n_predict)
      , beam_views(n_beams) {
        beams.reserve(n_beams);
        next_beams.reserve(n_beams);
    }

    // Collapse beams to a single beam given by index.
    void collapse_beams(const size_t beam_idx) {
        if (0u < beam_idx) {
            std::swap(beams[0], beams[beam_idx]);
        }
        beams.resize(1);
    }

    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
    // The repetitive patterns below reflect the 2 stages of heaps:
    //  * Gather elements until the vector is full, then call std::make_heap() on it.
    //  * If the heap is full and a new element is found that should be included, pop the
    //    least element to the back(), replace it with the new, then push it into the heap.
    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
        // Min-heaps use a greater-than comparator.
        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
        if (beam.eob) {
            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
            if (next_beams.size() < n_beams) {
                next_beams.push_back(std::move(beam));
                if (next_beams.size() == n_beams) {
                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
                }
            } else if (next_beams.front().p < beam.p) {
                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
                next_beams.back() = std::move(beam);
                std::push_heap(next_beams.begin(), next_beams.end(), comp);
            }
        } else {
            // beam is not at end-of-sentence, so branch with next top_k tokens.
            if (!beam.tokens.empty()) {
                llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
            }
            llama_logit_info logit_info(ctx);
            std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
            size_t i=0;
            if (next_beams.size() < n_beams) {
                for (; next_beams.size() < n_beams ; ++i) {
                    llama_beam next_beam = beam;
                    next_beam.tokens.push_back(next_tokens[i].id);
                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
                    next_beams.push_back(std::move(next_beam));
                }
                std::make_heap(next_beams.begin(), next_beams.end(), comp);
            } else {
                for (; next_beams.front().p == 0.0f ; ++i) {
                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
                    next_beams.back() = beam;
                    next_beams.back().tokens.push_back(next_tokens[i].id);
                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
                }
            }
            for (; i < n_beams ; ++i) {
                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
                if (next_beams.front().p < next_p) {
                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
                    next_beams.back() = beam;
                    next_beams.back().tokens.push_back(next_tokens[i].id);
                    next_beams.back().p = next_p;
                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
                }
            }
        }
    }

    // Find common_prefix_length based on beams.
    // Requires beams is not empty.
    size_t find_common_prefix_length() {
        size_t common_prefix_length = beams[0].tokens.size();
        for (size_t i = 1 ; i < beams.size() ; ++i) {
            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
                if (beams[0].tokens[j] != beams[i].tokens[j]) {
                    common_prefix_length = j;
                    break;
                }
            }
        }
        return common_prefix_length;
    }

    // Construct beams_state to send back to caller via the callback function.
    // Side effect: set common_prefix_length = find_common_prefix_length();
    llama_beams_state get_beams_state(const bool last_call) {
        for (size_t i = 0 ; i < beams.size() ; ++i) {
            beam_views[i] = beams[i].view();
        }
        common_prefix_length = find_common_prefix_length();
        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
    }

    // Loop:
    //  * while i < n_predict, AND
    //  * any of the beams have not yet reached end-of-beam (eob), AND
    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
    //    (since all other beam probabilities can only decrease)
    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
                       !beams[top_beam_index()].eob ; ++i) {
            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
            if (common_prefix_length) {
                llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
                n_past += common_prefix_length;
            }
            // Zero-out next_beam probabilities to place them last in following min-heap.
            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
            for (llama_beam & beam : beams) {
                beam.shift_tokens(common_prefix_length);
                fill_next_beams_by_top_probabilities(beam);
            }
            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
            beams.swap(next_beams);
            renormalize_beam_probabilities(beams);
        }
        collapse_beams(top_beam_index());
        callback(callback_data, get_beams_state(true));
    }

    // As beams grow, the cumulative probabilities decrease.
    // Renormalize them to avoid floating point underflow.
    static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
    }

    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
    size_t top_beam_index() {
        return std::max_element(beams.begin(), beams.end()) - beams.begin();
    }

    // Copy (p,eob) for each beam which may have been changed by the callback.
    void update_beams_from_beam_views() {
        for (size_t i = 0 ; i < beams.size() ; ++i) {
            beams[i].p = beam_views[i].p;
            beams[i].eob = beam_views[i].eob;
        }
    }
};

void llama_beam_search(llama_context * ctx,
                       llama_beam_search_callback_fn_t callback, void * callback_data,
                       size_t n_beams, int n_past, int n_predict) {
    assert(ctx);
    const int64_t t_start_sample_us = ggml_time_us();

    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);

    beam_search_data.loop(callback, callback_data);

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    ctx->n_sample++;
}

//
// quantization
//

struct quantize_state_internal {
    const llama_model                 & model;
    const llama_model_quantize_params * params;

    int n_attention_wv    = 0;
    int n_ffn_down        = 0;
    int n_ffn_gate        = 0;
    int n_ffn_up          = 0;
    int i_attention_wv    = 0;
    int i_ffn_down        = 0;
    int i_ffn_gate        = 0;
    int i_ffn_up          = 0;

    int n_k_quantized     = 0;
    int n_fallback        = 0;

    bool has_imatrix      = false;

    // used to figure out if a model shares tok_embd with the output weight
    bool has_output       = false;

    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
        : model(model)
        , params(params)
        {}
};

static void llama_tensor_dequantize_internal(
    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
    const size_t nelements, const int nthread
) {
    if (output.size() < nelements) {
        output.resize(nelements);
    }
    float * f32_output = (float *) output.data();

    ggml_type_traits_t qtype;
    if (ggml_is_quantized(tensor->type)) {
        qtype = ggml_internal_get_type_traits(tensor->type);
        if (qtype.to_float == NULL) {
            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
        }
    } else if (tensor->type != GGML_TYPE_F16) {
        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
    }

    if (nthread < 2) {
        if (tensor->type == GGML_TYPE_F16) {
            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
        } else if (ggml_is_quantized(tensor->type)) {
            qtype.to_float(tensor->data, f32_output, nelements);
        } else {
            GGML_ASSERT(false); // unreachable
        }
        return;
    }

    size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
    size_t block_size_bytes = ggml_type_size(tensor->type);

    GGML_ASSERT(nelements % block_size == 0);
    size_t nblocks = nelements / block_size;
    size_t blocks_per_thread = nblocks / nthread;
    size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count

    size_t in_buff_offs = 0;
    size_t out_buff_offs = 0;

    for (int tnum = 0; tnum < nthread; tnum++) {
        size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
        size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
        size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread

        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
            if (typ == GGML_TYPE_F16) {
                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
            } else {
                qtype.to_float(inbuf, outbuf, nels);
            }
        };
        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
        in_buff_offs += thr_block_bytes;
        out_buff_offs += thr_elems;
    }
    for (auto & w : workers) { w.join(); }
    workers.clear();
}

static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
    const std::string name = ggml_get_name(tensor);

    // TODO: avoid hardcoded tensor names - use the TN_* constants
    const llm_arch arch = qs.model.arch;
    const auto       tn = LLM_TN(arch);

    auto use_more_bits = [](int i_layer, int num_layers) -> bool {
        return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
    };
    const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
    auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
        if (n_expert > 1) {
            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
            // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
            // for getting the current layer as I initially thought, and we need to resort to parsing the
            // tensor name.
            n_layer /= n_expert;
            if (sscanf(name, "blk.%d.", &i_layer) != 1) {
                throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
            }
            if (i_layer < 0 || i_layer >= n_layer) {
                throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
            }
        }
        return std::make_pair(i_layer, n_layer);
    };

    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
    // with the quantization of the output tensor
    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
        int nx = tensor->ne[0];
        if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
            new_type = GGML_TYPE_Q8_0;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
                 ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
            new_type = GGML_TYPE_Q5_K;
        }
        else if (new_type != GGML_TYPE_Q8_0) {
            new_type = GGML_TYPE_Q6_K;
        }
    } else if (name == "token_embd.weight") {
        if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
            ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
            new_type = GGML_TYPE_Q2_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
            new_type = GGML_TYPE_IQ3_S;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
            new_type = GGML_TYPE_IQ3_S;
        }
    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
        if (name.find("attn_v.weight") != std::string::npos) {
            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
            ++qs.i_attention_wv;
        }
        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (name.find("ffn_down") != std::string::npos) {
            if (qs.i_ffn_down < qs.n_ffn_down/8) {
                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
            }
            ++qs.i_ffn_down;
        }
        else if (name.find("attn_output.weight") != std::string::npos) {
            if (qs.model.hparams.n_expert == 8) {
                new_type = GGML_TYPE_Q5_K;
            } else {
                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ2_XXS;
                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
            }
        }
    } else if (name.find("attn_v.weight") != std::string::npos) {
        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
            new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
            new_type = GGML_TYPE_Q5_K;
        }
        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
        else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
                (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
        if (qs.model.type == MODEL_70B) {
            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
            // nearly negligible increase in model size by quantizing this tensor with more bits:
            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
        }
        if (qs.model.hparams.n_expert == 8) {
            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
            // TODO: explore better strategies
            new_type = GGML_TYPE_Q8_0;
        }
        ++qs.i_attention_wv;
    } else if (name.find("attn_k.weight") != std::string::npos) {
        if (qs.model.hparams.n_expert == 8) {
            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
            // TODO: explore better strategies
            new_type = GGML_TYPE_Q8_0;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
            new_type = GGML_TYPE_IQ3_XXS;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
            new_type = GGML_TYPE_IQ2_S;
        }
    } else if (name.find("attn_q.weight") != std::string::npos) {
        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
            new_type = GGML_TYPE_IQ3_XXS;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
            new_type = GGML_TYPE_IQ2_S;
        }
    } else if (name.find("ffn_down") != std::string::npos) {
        auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
        int i_layer = info.first, n_layer = info.second;
        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
            if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
            new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
            new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
                     : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
                     : GGML_TYPE_Q3_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
                    (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
            new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
            if (arch == LLM_ARCH_FALCON) {
                new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
                           use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
            } else {
                if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
            }
        }
        else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
            new_type = GGML_TYPE_Q5_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
            new_type = GGML_TYPE_Q5_K;
        }
        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
                && qs.has_imatrix && i_layer < n_layer/8) {
            // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
            // We only do it when an imatrix is provided because a) we want to make sure that one can always get the
            // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
            new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
        }
        ++qs.i_ffn_down;
    } else if (name.find("attn_output.weight") != std::string::npos) {
        if (arch != LLM_ARCH_FALCON) {
            if (qs.model.hparams.n_expert == 8) {
                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
                    ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
                    new_type = GGML_TYPE_Q5_K;
                }
            } else {
                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   ) new_type = GGML_TYPE_Q3_K;
                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_Q4_K;
            }
        } else {
            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
        }
    }
    else if (name.find("attn_qkv.weight") != std::string::npos) {
        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
    }
    else if (name.find("ffn_gate") != std::string::npos) {
        auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
        int i_layer = info.first, n_layer = info.second;
        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
            new_type = GGML_TYPE_IQ3_XXS;
        }
        ++qs.i_ffn_gate;
    }
    else if (name.find("ffn_up") != std::string::npos) {
        auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
        int i_layer = info.first, n_layer = info.second;
        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
            new_type = GGML_TYPE_IQ3_XXS;
        }
        ++qs.i_ffn_up;
    }

    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
    //}
    // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
    //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
    //}
    // This can be used to reduce the size of the Q5_K_S model.
    // The associated PPL increase is fully in line with the size reduction
    //else {
    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
    //}
    bool convert_incompatible_tensor = false;
    if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
        new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
        new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) {
        int nx = tensor->ne[0];
        int ny = tensor->ne[1];
        if (nx % QK_K != 0) {
            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
            convert_incompatible_tensor = true;
        } else {
            ++qs.n_k_quantized;
        }
    }
    if (convert_incompatible_tensor) {
        switch (new_type) {
            case GGML_TYPE_IQ2_XXS:
            case GGML_TYPE_IQ2_XS:
            case GGML_TYPE_IQ2_S:
            case GGML_TYPE_IQ3_XXS:
            case GGML_TYPE_IQ3_S:
            case GGML_TYPE_IQ1_S:
            case GGML_TYPE_Q2_K:
            case GGML_TYPE_Q3_K:
            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
        }
        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
        ++qs.n_fallback;
    }

    return new_type;
}

static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
    std::mutex mutex;
    int counter = 0;
    size_t new_size = 0;
    if (nthread < 2) {
        // single-thread
        return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
    }
    auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
            nrows, n_per_row, imatrix]() {
        const int nrows_per_chunk = chunk_size / n_per_row;
        size_t local_size = 0;
        while (true) {
            std::unique_lock<std::mutex> lock(mutex);
            int first_row = counter; counter += nrows_per_chunk;
            if (first_row >= nrows) {
                if (local_size > 0) {
                    new_size += local_size;
                }
                break;
            }
            lock.unlock();
            const int this_nrow = std::min(nrows - first_row, nrows_per_chunk);
            local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
        }
    };
    for (int it = 0; it < nthread - 1; ++it) {
        workers.emplace_back(compute);
    }
    compute();
    for (auto & w : workers) { w.join(); }
    workers.clear();
    return new_size;
}

static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
    ggml_type default_type;
    llama_ftype ftype = params->ftype;

    switch (params->ftype) {
        case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
        case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
        case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
        case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
        case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
        case LLAMA_FTYPE_MOSTLY_F16:  default_type = GGML_TYPE_F16;  break;
        case LLAMA_FTYPE_ALL_F32:     default_type = GGML_TYPE_F32;  break;

        // K-quants
        case LLAMA_FTYPE_MOSTLY_Q2_K_S:
        case LLAMA_FTYPE_MOSTLY_Q2_K:    default_type = GGML_TYPE_Q2_K;    break;
        case LLAMA_FTYPE_MOSTLY_IQ3_XS:  default_type = GGML_TYPE_IQ3_S;   break;
        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  default_type = GGML_TYPE_Q3_K;    break;
        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  default_type = GGML_TYPE_Q4_K;    break;
        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  default_type = GGML_TYPE_Q5_K;    break;
        case LLAMA_FTYPE_MOSTLY_Q6_K:    default_type = GGML_TYPE_Q6_K;    break;
        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  default_type = GGML_TYPE_IQ2_XS;  break;
        case LLAMA_FTYPE_MOSTLY_IQ2_S:   default_type = GGML_TYPE_IQ2_XS;  break;
        case LLAMA_FTYPE_MOSTLY_IQ2_M:   default_type = GGML_TYPE_IQ2_S;   break;
        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
        case LLAMA_FTYPE_MOSTLY_IQ1_S:   default_type = GGML_TYPE_IQ1_S;   break;
        case LLAMA_FTYPE_MOSTLY_IQ4_NL:  default_type = GGML_TYPE_IQ4_NL;  break;
        case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
        case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
        case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;

        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
    }

    int nthread = params->nthread;

    if (nthread <= 0) {
        nthread = std::thread::hardware_concurrency();
    }

    // mmap consistently increases speed Linux, and also increases speed on Windows with
    // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
#if defined(__linux__) || defined(_WIN32)
    constexpr bool use_mmap = true;
#else
    constexpr bool use_mmap = false;
#endif

    llama_model_loader ml(fname_inp, use_mmap, NULL);
    ml.init_mapping(false); // no prefetching?

    llama_model model;
    llm_load_arch(ml, model);
    llm_load_hparams(ml, model);

    struct quantize_state_internal qs(model, params);

    if (params->only_copy) {
        ftype = model.ftype;
    }
    const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
    if (params->imatrix) {
        imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
        if (imatrix_data) {
            LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
            qs.has_imatrix = true;
        }
    }

    const size_t align = GGUF_DEFAULT_ALIGNMENT;
    struct gguf_context * ctx_out = gguf_init_empty();

    // copy the KV pairs from the input file
    gguf_set_kv     (ctx_out, ml.ctx_gguf);
    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
    gguf_set_val_u32(ctx_out, "general.file_type", ftype);

    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * meta = ml.get_tensor_meta(i);

        const std::string name = ggml_get_name(meta);

        // TODO: avoid hardcoded tensor names - use the TN_* constants
        if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
            ++qs.n_attention_wv;
        }
        else if (name.find("ffn_down") != std::string::npos) {
            ++qs.n_ffn_down;
        }
        else if (name.find("ffn_gate") != std::string::npos) {
            ++qs.n_ffn_gate;
        }
        else if (name.find("ffn_up") != std::string::npos) {
            ++qs.n_ffn_up;
        }
        else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
            qs.has_output = true;
        }
    }
    if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) {
        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n",
                __func__, qs.n_attention_wv, qs.n_ffn_down, model.hparams.n_layer);
    }

    size_t total_size_org = 0;
    size_t total_size_new = 0;

    std::vector<std::thread> workers;
    workers.reserve(nthread);

    int idx = 0;

    std::vector<no_init<uint8_t>> read_data;
    std::vector<no_init<uint8_t>> work;
    std::vector<no_init<float>> f32_conv_buf;

    // populate the original tensors so we get an initial meta data
    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * meta = ml.get_tensor_meta(i);
        gguf_add_tensor(ctx_out, meta);
    }

    std::ofstream fout(fname_out, std::ios::binary);
    fout.exceptions(std::ofstream::failbit); // fail fast on write errors

    const size_t meta_size = gguf_get_meta_size(ctx_out);

    LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);

    // placeholder for the meta data
    ::zeros(fout, meta_size);

    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * tensor = ml.get_tensor_meta(i);

        const std::string name = ggml_get_name(tensor);

        if (!ml.use_mmap) {
            if (read_data.size() < ggml_nbytes(tensor)) {
                read_data.resize(ggml_nbytes(tensor));
            }
            tensor->data = read_data.data();
        }
        ml.load_data_for(tensor);

        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
               ++idx, ml.n_tensors,
               ggml_get_name(tensor),
               llama_format_tensor_shape(tensor).c_str(),
               ggml_type_name(tensor->type));

        // This used to be a regex, but <regex> has an extreme cost to compile times.
        bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?

        // quantize only 2D tensors
        quantize &= (ggml_n_dims(tensor) == 2);
        quantize &= params->quantize_output_tensor || name != "output.weight";
        quantize &= !params->only_copy;

        // do not quantize expert gating tensors
        // NOTE: can't use LLM_TN here because the layer number is not known
        quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;

        // do not quantize positional embeddings and token types (BERT)
        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD,    "weight");
        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");

        // do not quantize Mamba's small yet 2D weights
        // NOTE: can't use LLM_TN here because the layer number is not known
        quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
        quantize &= name.find("ssm_x.weight")      == std::string::npos;
        quantize &= name.find("ssm_dt.weight")     == std::string::npos;

        enum ggml_type new_type;
        void * new_data;
        size_t new_size;

        if (quantize) {
            new_type = default_type;

            // get more optimal quantization type based on the tensor shape, layer, etc.
            if (!params->pure && ggml_is_quantized(default_type)) {
                new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
            }

            // If we've decided to quantize to the same type the tensor is already
            // in then there's nothing to do.
            quantize = tensor->type != new_type;
        }

        if (!quantize) {
            new_type = tensor->type;
            new_data = tensor->data;
            new_size = ggml_nbytes(tensor);
            LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
        } else {
            const size_t nelements = ggml_nelements(tensor);

            const float * imatrix = nullptr;
            if (imatrix_data) {
                auto it = imatrix_data->find(tensor->name);
                if (it == imatrix_data->end()) {
                    LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
                } else {
                    if (it->second.size() == (size_t)tensor->ne[0]) {
                        imatrix = it->second.data();
                    } else {
                        LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
                                int(it->second.size()), int(tensor->ne[0]), tensor->name);
                    }
                }
            }
            if ((new_type == GGML_TYPE_IQ2_XXS ||
                 new_type == GGML_TYPE_IQ2_XS  ||
                 new_type == GGML_TYPE_IQ2_S   ||
                 new_type == GGML_TYPE_IQ1_S   ||
                (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
                LLAMA_LOG_ERROR("\n\n============================================================\n");
                LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
                LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
                LLAMA_LOG_ERROR("============================================================\n\n");
                throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
            }

            float * f32_data;

            if (tensor->type == GGML_TYPE_F32) {
                f32_data = (float *) tensor->data;
            } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
                throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
            } else {
                llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
                f32_data = (float *) f32_conv_buf.data();
            }

            LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
            fflush(stdout);

            if (work.size() < nelements * 4) {
                work.resize(nelements * 4); // upper bound on size
            }
            new_data = work.data();

            const int n_per_row = tensor->ne[0];
            const int nrows = nelements / n_per_row;

            static const int min_chunk_size = 32 * 512;
            const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);

            const int nchunk = (nelements + chunk_size - 1)/chunk_size;
            const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
            new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix, workers, nthread_use);

            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
        }
        total_size_org += ggml_nbytes(tensor);
        total_size_new += new_size;

        // update the gguf meta data as we go
        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);

        // write tensor data + padding
        fout.write((const char *) new_data, new_size);
        zeros(fout, GGML_PAD(new_size, align) - new_size);
    }

    // go back to beginning of file and write the updated meta data
    {
        fout.seekp(0);
        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
        gguf_get_meta_data(ctx_out, data.data());
        fout.write((const char *) data.data(), data.size());
    }

    fout.close();

    gguf_free(ctx_out);

    LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
    LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);

    if (qs.n_fallback > 0) {
        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
                __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
    }
}

static int llama_apply_lora_from_file_internal(
    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
) {
    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);

    const int64_t t_start_lora_us = ggml_time_us();

    llama_file fin(path_lora, "rb");

    // verify magic and version
    {
        uint32_t magic = fin.read_u32();
        if (magic != LLAMA_FILE_MAGIC_GGLA) {
            LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
            return 1;
        }

        uint32_t format_version = fin.read_u32();
        if (format_version != 1) {
            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
            return 1;
        }
    }

    int32_t lora_r = fin.read_u32();
    int32_t lora_alpha = fin.read_u32();
    float scaling = scale * (float)lora_alpha / (float)lora_r;

    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);

    // load base model
    std::unique_ptr<llama_model_loader> ml;
    if (path_base_model) {
        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
        ml->init_mapping(/*prefetch*/ false); // no prefetching
    }

    struct tensor_meta {
        std::string name;
        ggml_type type;
        int32_t ne[2];
        size_t offset;
    };
    std::map<std::string, tensor_meta> tensor_meta_map;

    // load all tensor meta
    while (true) {
        if (fin.tell() == fin.size) {
            // eof
            break;
        }

        int32_t n_dims;
        int32_t name_len;
        int32_t ftype;

        fin.read_raw(&n_dims, sizeof(n_dims));
        fin.read_raw(&name_len, sizeof(name_len));
        fin.read_raw(&ftype, sizeof(ftype));

        if (n_dims != 1 && n_dims != 2) {
            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
            return 1;
        }

        int32_t ne[2] = { 1, 1 };
        for (int i = 0; i < n_dims; ++i) {
            fin.read_raw(&ne[i], sizeof(ne[i]));
        }

        std::string name;
        {
            GGML_ASSERT(name_len < GGML_MAX_NAME);
            char buf[GGML_MAX_NAME];
            fin.read_raw(buf, name_len);
            name = std::string(buf, name_len);
        }

        // check for lora suffix
        std::string lora_suffix;
        if (name.length() > 6) {
            lora_suffix = name.substr(name.length() - 6);
        }
        if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
            return 1;
        }

        // tensor type
        ggml_type wtype;
        switch (ftype) {
            case 0: wtype = GGML_TYPE_F32;  break;
            case 1: wtype = GGML_TYPE_F16;  break;
            default:
                    {
                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
                                __func__, ftype);
                        return 1;
                    }
        }

        // data offset
        size_t offset = fin.tell();
        offset = (offset + 31) & -32;

        // skip tensor data
        fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);

        tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
    }

    bool warned = false;
    int n_tensors = 0;

    // apply
    ggml_backend_t backend_cpu = ggml_backend_cpu_init();
    if (backend_cpu == nullptr) {
        LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
        return 1;
    }
    ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);

    std::vector<no_init<uint8_t>> read_buf;
    for (const auto & it : model.tensors_by_name) {
        const std::string & base_name = it.first;
        ggml_tensor * model_t = it.second;

        if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
            tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
            continue;
        }

        tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
        tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");

        ggml_init_params lora_init_params = {
            /* .mem_size   */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
            /* .mem_buffer */ nullptr,
            /* .no_alloc   */ true,
        };
        ggml_context * lora_ctx = ggml_init(lora_init_params);
        if (lora_ctx == nullptr) {
            LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
            ggml_backend_free(backend_cpu);
            return 1;
        }

        // create tensors
        ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
        ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
        ggml_set_name(loraA, metaA.name.c_str());
        ggml_set_name(loraB, metaB.name.c_str());

        ggml_tensor * base_t;
        if (ml) {
            if (gguf_find_tensor(ml->ctx_gguf, base_name.c_str()) < 0) {
                LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
                return 1;
            }
            base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
        } else {
            base_t = ggml_dup_tensor(lora_ctx, model_t);
        }
        ggml_set_name(base_t, base_name.c_str());

        // allocate in backend buffer
        ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
        if (lora_buf == nullptr) {
            LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
            return 1;
        }

        // load tensor data
        auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
            read_buf.resize(ggml_nbytes(tensor));
            fin.seek(tensor_meta.offset, SEEK_SET);
            fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
            ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
        };
        load_tensor(metaA, loraA);
        load_tensor(metaB, loraB);

        // load base model tensor data
        if (ml) {
            ml->load_data_for(base_t);
        } else {
            ggml_backend_tensor_copy(model_t, base_t);
        }

        if (ggml_is_quantized(base_t->type) && !warned) {
            LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
                            "use a f16 or f32 base model with --lora-base\n", __func__);
            warned = true;
        }

        if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
            ggml_free(lora_ctx);
            ggml_backend_buffer_free(lora_buf);
            ggml_backend_free(backend_cpu);
            return 1;
        }

        auto build_lora_graph = [&]() {
            // w = w + BA*s
            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
            ggml_set_name(BA, "BA");

            if (scaling != 1.0f) {
                BA = ggml_scale(lora_ctx, BA, scaling);
                ggml_set_name(BA, "BA_scaled");
            }

            ggml_tensor * r;
            r = ggml_add_inplace(lora_ctx, base_t, BA);
            ggml_set_name(r, "r_add");

            if (base_t->type != model_t->type) {
                // convert the result to the model type
                r = ggml_cast(lora_ctx, r, model_t->type);
                ggml_set_name(r, "r_cast");
            }

            return r;
        };

        ggml_cgraph * gf = ggml_new_graph(lora_ctx);
        ggml_tensor * r = build_lora_graph();
        ggml_build_forward_expand(gf, r);

        ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
        if (graph_buf == nullptr) {
            LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
            ggml_free(lora_ctx);
            ggml_backend_buffer_free(lora_buf);
            ggml_backend_free(backend_cpu);
            return 1;
        }

        ggml_backend_graph_compute(backend_cpu, gf);

        ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));

#if 0
        // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
        //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);

        // sched compute
        ggml_build_forward_expand(gf, build_graph());
        ggml_backend_sched_init_measure(sched, gf);

        // create the graph again, since the previous one was destroyed by the measure
        ggml_graph_clear(gf);
        ggml_build_forward_expand(gf, build_graph());
        ggml_backend_sched_graph_compute(sched, gf);
        ggml_backend_sched_free(sched);
#endif

        ggml_backend_buffer_free(lora_buf);
        ggml_backend_buffer_free(graph_buf);
        ggml_free(lora_ctx);

        n_tensors++;
        if (n_tensors % 4 == 0) {
            LLAMA_LOG_INFO(".");
        }
    }

    ggml_backend_free(backend_cpu);

    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);

    return 0;
}

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

struct llama_context_params llama_context_default_params() {
    struct llama_context_params result = {
        /*.seed                        =*/ LLAMA_DEFAULT_SEED,
        /*.n_ctx                       =*/ 512,
        /*.n_batch                     =*/ 4096,
        /*.n_ubatch                    =*/ 512,
        /*.n_seq_max                   =*/ 1,
        /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
        /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
        /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
        /*.rope_freq_base              =*/ 0.0f,
        /*.rope_freq_scale             =*/ 0.0f,
        /*.yarn_ext_factor             =*/ -1.0f,
        /*.yarn_attn_factor            =*/ 1.0f,
        /*.yarn_beta_fast              =*/ 32.0f,
        /*.yarn_beta_slow              =*/ 1.0f,
        /*.yarn_orig_ctx               =*/ 0,
        /*.defrag_thold                =*/ -1.0f,
        /*.cb_eval                     =*/ nullptr,
        /*.cb_eval_user_data           =*/ nullptr,
        /*.type_k                      =*/ GGML_TYPE_F16,
        /*.type_v                      =*/ GGML_TYPE_F16,
        /*.logits_all                  =*/ false,
        /*.embeddings                  =*/ false,
        /*.offload_kqv                 =*/ true,
        /*.abort_callback              =*/ nullptr,
        /*.abort_callback_data         =*/ nullptr,
    };

    return result;
}

struct llama_model_quantize_params llama_model_quantize_default_params() {
    struct llama_model_quantize_params result = {
        /*.nthread                     =*/ 0,
        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
        /*.allow_requantize            =*/ false,
        /*.quantize_output_tensor      =*/ true,
        /*.only_copy                   =*/ false,
        /*.pure                        =*/ false,
        /*.imatrix                     =*/ nullptr,
    };

    return result;
}

size_t llama_max_devices(void) {
#if defined(GGML_USE_METAL)
    return 1;
#elif defined(GGML_USE_CUBLAS)
    return GGML_CUDA_MAX_DEVICES;
#elif defined(GGML_USE_SYCL)
    return GGML_SYCL_MAX_DEVICES;
#elif defined(GGML_USE_VULKAN)
    return GGML_VK_MAX_DEVICES;
#else
    return 1;
#endif
}

bool llama_supports_mmap(void) {
    return llama_mmap::SUPPORTED;
}

bool llama_supports_mlock(void) {
    return llama_mlock::SUPPORTED;
}

bool llama_supports_gpu_offload(void) {
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
    defined(GGML_USE_SYCL)   || defined(GGML_USE_KOMPUTE)
    // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
    return true;
#else
    return false;
#endif
}

void llama_backend_init(void) {
    ggml_time_init();

    // needed to initialize f16 tables
    {
        struct ggml_init_params params = { 0, NULL, false };
        struct ggml_context * ctx = ggml_init(params);
        ggml_free(ctx);
    }

#ifdef GGML_USE_MPI
    ggml_mpi_backend_init();
#endif
}

void llama_numa_init(enum ggml_numa_strategy numa) {
    if (numa != GGML_NUMA_STRATEGY_DISABLED) {
        ggml_numa_init(numa);
    }
}

void llama_backend_free(void) {
#ifdef GGML_USE_MPI
    ggml_mpi_backend_free();
#endif
    ggml_quantize_free();
}

int64_t llama_time_us(void) {
    return ggml_time_us();
}

struct llama_model * llama_load_model_from_file(
        const char * path_model,
        struct llama_model_params   params) {
    ggml_time_init();

    llama_model * model = new llama_model;

    unsigned cur_percentage = 0;
    if (params.progress_callback == NULL) {
        params.progress_callback_user_data = &cur_percentage;
        params.progress_callback = [](float progress, void * ctx) {
            unsigned * cur_percentage_p = (unsigned *) ctx;
            unsigned percentage = (unsigned) (100 * progress);
            while (percentage > *cur_percentage_p) {
                *cur_percentage_p = percentage;
                LLAMA_LOG_INFO(".");
                if (percentage >= 100) {
                    LLAMA_LOG_INFO("\n");
                }
            }
            return true;
        };
    }

    int status = llama_model_load(path_model, *model, params);
    GGML_ASSERT(status <= 0);
    if (status < 0) {
        if (status == -1) {
            LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
        } else if (status == -2) {
            LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
        }
        delete model;
        return nullptr;
    }

    return model;
}

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

struct llama_context * llama_new_context_with_model(
                 struct llama_model * model,
        struct llama_context_params   params) {

    if (!model) {
        LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
        return nullptr;
    }

    if (params.n_batch == 0 && params.n_ubatch == 0) {
        LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
        return nullptr;
    }

    if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
        LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
        return nullptr;
    }

    llama_context * ctx = new llama_context(*model);

    const auto & hparams = model->hparams;
    auto       & cparams = ctx->cparams;

    // TODO: maybe add n_seq_max here too
    cparams.n_batch          = params.n_batch;
    cparams.n_ubatch         = params.n_ubatch == 0 ? params.n_batch : params.n_ubatch;
    cparams.n_threads        = params.n_threads;
    cparams.n_threads_batch  = params.n_threads_batch;
    cparams.yarn_ext_factor  = params.yarn_ext_factor;
    cparams.yarn_attn_factor = params.yarn_attn_factor;
    cparams.yarn_beta_fast   = params.yarn_beta_fast;
    cparams.yarn_beta_slow   = params.yarn_beta_slow;
    cparams.defrag_thold     = params.defrag_thold;
    cparams.embeddings       = params.embeddings;
    cparams.offload_kqv      = params.offload_kqv;
    cparams.pooling_type     = params.pooling_type;

    cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
    cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
    cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;

    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
                               hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
                                                              hparams.n_ctx_train;

    cparams.cb_eval           = params.cb_eval;
    cparams.cb_eval_user_data = params.cb_eval_user_data;

    auto rope_scaling_type = params.rope_scaling_type;
    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
        rope_scaling_type = hparams.rope_scaling_type_train;
    }

    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
        cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
    }

    if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
    }

    cparams.causal_attn = hparams.causal_attn;

    if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
        if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
            cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
        } else {
            cparams.pooling_type = hparams.pooling_type;
        }
    }

    if (params.seed == LLAMA_DEFAULT_SEED) {
        params.seed = time(NULL);
    }

    LLAMA_LOG_INFO("%s: n_ctx      = %u\n",     __func__, cparams.n_ctx);
    LLAMA_LOG_INFO("%s: n_batch    = %u\n",     __func__, cparams.n_batch);
    LLAMA_LOG_INFO("%s: n_ubatch   = %u\n",     __func__, cparams.n_ubatch);
    LLAMA_LOG_INFO("%s: freq_base  = %.1f\n",   __func__, cparams.rope_freq_base);
    LLAMA_LOG_INFO("%s: freq_scale = %g\n",     __func__, cparams.rope_freq_scale);

    ctx->abort_callback      = params.abort_callback;
    ctx->abort_callback_data = params.abort_callback_data;

    ctx->rng                 = std::mt19937(params.seed);
    ctx->logits_all          = params.logits_all;

    uint32_t kv_size = cparams.n_ctx;
    ggml_type type_k = params.type_k;
    ggml_type type_v = params.type_v;

    // Mamba only needs a constant number of KV cache cells per sequence
    if (model->arch == LLM_ARCH_MAMBA) {
        // Mamba needs at least as many KV cells as there are sequences kept at any time
        kv_size = std::max((uint32_t) 1, params.n_seq_max);
        // it's probably best to keep as much precision as possible for the states
        type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
        type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
    }

    GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
    GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);

    if (!hparams.vocab_only) {
        // initialize backends
#ifdef GGML_USE_METAL
        if (model->n_gpu_layers > 0) {
            ctx->backend_metal = ggml_backend_metal_init();
            if (ctx->backend_metal == nullptr) {
                LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
                llama_free(ctx);
                return nullptr;
            }
            ctx->backends.push_back(ctx->backend_metal);
        }
#elif defined(GGML_USE_CUBLAS)
        if (model->n_gpu_layers > 0) {
            // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
            if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
                ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
                if (backend == nullptr) {
                    LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
                    llama_free(ctx);
                    return nullptr;
                }
                ctx->backends.push_back(backend);
            } else {
                // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
                for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
                    ggml_backend_t backend = ggml_backend_cuda_init(device);
                    if (backend == nullptr) {
                        LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
                        llama_free(ctx);
                        return nullptr;
                    }
                    ctx->backends.push_back(backend);
                }
            }
        }
#elif defined(GGML_USE_VULKAN)
        if (model->n_gpu_layers > 0) {
            for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
                ggml_backend_t backend = ggml_backend_vk_init(device);
                if (backend == nullptr) {
                    LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
                    llama_free(ctx);
                    return nullptr;
                }
                ctx->backends.push_back(backend);
            }
        }
#elif defined(GGML_USE_SYCL)
        if (model->n_gpu_layers > 0) {
            // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
            if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
                int main_gpu_index = ggml_backend_sycl_get_device_index(model->main_gpu);
                ggml_backend_t backend = ggml_backend_sycl_init(main_gpu_index);
                if (backend == nullptr) {
                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, model->main_gpu, main_gpu_index);
                    llama_free(ctx);
                    return nullptr;
                }
                ctx->backends.push_back(backend);
            } else {
                // LLAMA_SPLIT_LAYER requires a backend for each GPU
                int id_list[GGML_SYCL_MAX_DEVICES];
                ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
                for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
                    int device_id = id_list[i];
                    ggml_backend_t backend = ggml_backend_sycl_init(i);
                    if (backend == nullptr) {
                        LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d)backend\n", __func__, device_id, i);
                        llama_free(ctx);
                        return nullptr;
                    }
                    ctx->backends.push_back(backend);
                }
            }
        }
#elif defined(GGML_USE_KOMPUTE)
        if (model->n_gpu_layers > 0) {
            auto * backend = ggml_backend_kompute_init(model->main_gpu);
            if (backend == nullptr) {
                LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__);
                llama_free(ctx);
                return nullptr;
            }
            ctx->backends.push_back(backend);
        }
#endif
        ctx->backend_cpu = ggml_backend_cpu_init();
        if (ctx->backend_cpu == nullptr) {
            LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
            llama_free(ctx);
            return nullptr;
        }
        ctx->backends.push_back(ctx->backend_cpu);

        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, cparams.offload_kqv)) {
            LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
            llama_free(ctx);
            return nullptr;
        }

        {
            size_t memory_size_k = 0;
            size_t memory_size_v = 0;

            for (auto & k : ctx->kv_self.k_l) {
                memory_size_k += ggml_nbytes(k);
            }

            for (auto & v : ctx->kv_self.v_l) {
                memory_size_v += ggml_nbytes(v);
            }

            LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
                (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
                ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
                ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
        }

        // graph outputs buffer
        {
            // resized during inference, reserve maximum
            ctx->logits_size = hparams.n_vocab*cparams.n_ctx;
            ctx->embd_size = params.embeddings ? hparams.n_embd*cparams.n_ctx : 0;

            const size_t buf_output_size = (ctx->logits_size + ctx->embd_size)*sizeof(float);

            ctx->buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buf_output_size);
            if (ctx->buf_output == nullptr) {
                LLAMA_LOG_ERROR("%s: failed to allocate logits buffer\n", __func__);
                llama_free(ctx);
                return nullptr;
            }
            ggml_backend_buffer_clear(ctx->buf_output, 0);


            ctx->logits = (float *) ggml_backend_buffer_get_base(ctx->buf_output);
            if (params.embeddings) {
                ctx->embd = ctx->logits + ctx->logits_size;
            }

            LLAMA_LOG_INFO("%s: %10s  output buffer size = %8.2f MiB\n", __func__,
                    ggml_backend_buffer_name(ctx->buf_output),
                    ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0);
        }

        // scheduler and compute buffers
        {
            // buffer types used for the compute buffer of each backend
            std::vector<ggml_backend_buffer_type_t> backend_buft;
            for (auto * backend : ctx->backends) {
                if (ggml_backend_is_cpu(backend)) {
                    // use host buffers for the CPU backend compute buffer
                    backend_buft.push_back(llama_default_buffer_type_cpu(true));
                } else {
                    backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
                }
            }

            // buffer used to store the computation graph and the tensor meta data
            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));

            // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
            bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER;
#ifndef GGML_USE_CUBLAS
            // pipeline parallelism requires support for async compute and events
            // currently this is only implemented in the CUDA backend
            pipeline_parallel = false;
#endif
            if (pipeline_parallel) {
                LLAMA_LOG_INFO("%s: pipeline parallelism enabled\n", __func__);
            }
            ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);

            // build worst-case graph
            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch);
            int n_past = cparams.n_ctx - n_tokens;
            llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
            ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);

            // initialize scheduler with the worst-case graph
            if (!ggml_backend_sched_reserve(ctx->sched, gf)) {
                LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
                llama_free(ctx);
                return nullptr;
            }

            for (size_t i = 0; i < ctx->backends.size(); i++) {
                ggml_backend_t backend = ctx->backends[i];
                ggml_backend_buffer_type_t buft = backend_buft[i];
                size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
                LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
                        ggml_backend_buft_name(buft),
                        size / 1024.0 / 1024.0);
            }

            // note: the number of splits during measure is higher than during inference due to the kv shift
            int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
            LLAMA_LOG_INFO("%s: graph splits: %d\n", __func__, n_splits);
        }
    }

#ifdef GGML_USE_MPI
    ctx->ctx_mpi = ggml_mpi_init();

    if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
        // Enter a blocking eval loop with dummy input, letting rank=0 drive the process
        // TODO: needs fix after #3228
        GGML_ASSERT(false && "not implemented");
        //const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx));
        //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
        llama_backend_free();
        exit(1);
    }
#endif

    return ctx;
}

void llama_free(struct llama_context * ctx) {
    delete ctx;
}

const llama_model * llama_get_model(const struct llama_context * ctx) {
    return &ctx->model;
}

uint32_t llama_n_ctx(const struct llama_context * ctx) {
    return ctx->cparams.n_ctx;
}

uint32_t llama_n_batch(const struct llama_context * ctx) {
    return ctx->cparams.n_batch;
}

uint32_t llama_n_seq_max(const struct llama_context * ctx) {
    return ctx->kv_self.size;
}

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

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_GPTNEOX:
        case LLM_ARCH_MPT:
        case LLM_ARCH_REFACT:
        case LLM_ARCH_BLOOM:
        case LLM_ARCH_MAMBA:
            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_BAICHUAN:
        case LLM_ARCH_STARCODER:
        case LLM_ARCH_PLAMO:
        case LLM_ARCH_CODESHELL:
        case LLM_ARCH_ORION:
        case LLM_ARCH_INTERNLM2:
        case LLM_ARCH_MINICPM:
            return LLAMA_ROPE_TYPE_NORM;

        // the pairs of head values are offset by n_rot/2
        case LLM_ARCH_FALCON:
        case LLM_ARCH_PERSIMMON:
        case LLM_ARCH_BERT:
        case LLM_ARCH_NOMIC_BERT:
        case LLM_ARCH_STABLELM:
        case LLM_ARCH_QWEN:
        case LLM_ARCH_QWEN2:
        case LLM_ARCH_PHI2:
        case LLM_ARCH_GEMMA:
        case LLM_ARCH_STARCODER2:
            return LLAMA_ROPE_TYPE_NEOX;

        // all model arches should be listed explicitly here
        case LLM_ARCH_UNKNOWN:
            GGML_ASSERT(false && "unknown architecture");
            break;
    }

    return LLAMA_ROPE_TYPE_NONE;
}

int32_t llama_n_vocab(const struct llama_model * model) {
    return model->vocab.id_to_token.size();
}

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;
}

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->arch),
            llama_model_type_name(model->type),
            llama_model_ftype_name(model->ftype).c_str());
}

uint64_t llama_model_size(const struct llama_model * model) {
    uint64_t size = 0;
    for (const auto & it : model->tensors_by_name) {
        size += ggml_nbytes(it.second);
    }
    return size;
}

uint64_t llama_model_n_params(const struct llama_model * model) {
    uint64_t nparams = 0;
    for (const auto & it : model->tensors_by_name) {
        nparams += ggml_nelements(it.second);
    }
    return nparams;
}

struct ggml_tensor * llama_get_model_tensor(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;
}

uint32_t llama_model_quantize(
        const char * fname_inp,
        const char * fname_out,
        const llama_model_quantize_params * params) {
    try {
        llama_model_quantize_internal(fname_inp, fname_out, params);
        return 0;
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
        return 1;
    }
}

int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
    try {
        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
        return 1;
    }
}

struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
    struct llama_kv_cache_view result = {
        /*.n_cells            = */ 0,
        /*.n_seq_max          = */ n_seq_max,
        /*.token_count        = */ 0,
        /*.used_cells         = */ llama_get_kv_cache_used_cells(ctx),
        /*.max_contiguous     = */ 0,
        /*.max_contiguous_idx = */ -1,
        /*.cells              = */ nullptr,
        /*.cells_sequences    = */ nullptr,
    };
    return result;
}

void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
    if (view->cells != nullptr) {
        free(view->cells);
        view->cells = nullptr;
    }
    if (view->cells_sequences != nullptr) {
        free(view->cells_sequences);
        view->cells_sequences = nullptr;
    }
}

void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) {
    if (uint32_t(view->n_cells) < ctx->kv_self.size || view->cells == nullptr) {
        view->n_cells = int32_t(ctx->kv_self.size);
        void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
        GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
        view->cells = (struct llama_kv_cache_view_cell *)p;
        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
        GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
        view->cells_sequences = (llama_seq_id *)p;
    }

    const std::vector<llama_kv_cell> & kv_cells = ctx->kv_self.cells;
    llama_kv_cache_view_cell * c_curr = view->cells;
    llama_seq_id * cs_curr = view->cells_sequences;
    int32_t used_cells = 0;
    int32_t token_count = 0;
    int32_t curr_contig_idx = -1;
    uint32_t max_contig = 0;
    int32_t max_contig_idx = -1;

    for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
        const size_t curr_size = kv_cells[i].seq_id.size();
        token_count += curr_size;
        c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;

        if (curr_size > 0) {
            if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
                max_contig = i - curr_contig_idx;
                max_contig_idx = curr_contig_idx;
            }
            curr_contig_idx = -1;
        } else if (curr_contig_idx < 0) {
            curr_contig_idx = i;
        }

        int seq_idx = 0;
        for (const llama_seq_id it : kv_cells[i].seq_id) {
            if (seq_idx >= view->n_seq_max) {
                break;
            }
            cs_curr[seq_idx] = it;
            seq_idx++;
        }
        if (seq_idx != 0) {
            used_cells++;
        }
        for (; seq_idx < view->n_seq_max; seq_idx++) {
            cs_curr[seq_idx] = -1;
        }
    }
    if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
        max_contig_idx = curr_contig_idx;
        max_contig = kv_cells.size() - curr_contig_idx;
    }
    view->max_contiguous = max_contig;
    view->max_contiguous_idx = max_contig_idx;
    view->token_count = token_count;
    view->used_cells = used_cells;
    if (uint32_t(used_cells) != ctx->kv_self.used) {
        LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
            __func__, ctx->kv_self.used, used_cells);
    }
}

int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
    int result = 0;

    for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
        result += ctx->kv_self.cells[i].seq_id.size();
    }

    return result;
}

int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
    return ctx->kv_self.used;
}

void llama_kv_cache_clear(struct llama_context * ctx) {
    llama_kv_cache_clear(ctx->kv_self);
}

bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
    return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
}

void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
    if (seq_id_src == seq_id_dst) {
        return;
    }
    llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
}

void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
    llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
}

void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
    if (delta == 0) {
        return;
    }

    llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta);
}

void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
    if (d == 1) {
        return;
    }

    llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
}

llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
    return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
}

void llama_kv_cache_defrag(struct llama_context * ctx) {
    llama_kv_cache_defrag(ctx->kv_self);
}

void llama_kv_cache_update(struct llama_context * ctx) {
    llama_kv_cache_update_internal(*ctx);
}


// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
    // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
    // for reference, std::mt19937(1337) serializes to 6701 bytes.
    const size_t s_rng_size        = sizeof(size_t);
    const size_t s_rng             = LLAMA_MAX_RNG_STATE;
    const size_t s_logits_size     = sizeof(size_t);
    // assume worst case for logits although only currently set ones are serialized
    const size_t s_logits          = ctx->logits_size * sizeof(float);
    const size_t s_embedding_size  = sizeof(size_t);
    const size_t s_embedding       = ctx->embd_size * sizeof(float);
    const size_t s_kv_buf_size     = sizeof(size_t);
    const size_t s_kv_head         = sizeof(uint32_t);
    const size_t s_kv_size         = sizeof(uint32_t);
    const size_t s_kv_used         = sizeof(uint32_t);
    const size_t s_kv              = ctx->kv_self.total_size();
    // TODO: assume the max is more than 1 seq_id per KV cell
    const size_t s_kv_cell         = sizeof(llama_pos) + sizeof(size_t) + sizeof(llama_seq_id);
    const size_t s_kv_cells        = ctx->kv_self.size * s_kv_cell;

    const size_t s_total = (
        + s_rng_size
        + s_rng
        + s_logits_size
        + s_logits
        + s_embedding_size
        + s_embedding
        + s_kv_buf_size
        + s_kv_head
        + s_kv_size
        + s_kv_used
        + s_kv
        + s_kv_cells
    );

    return s_total;
}

// llama_context_data
struct llama_data_context {
    virtual void write(const void * src, size_t size) = 0;
    virtual size_t get_size_written() = 0;
    virtual ~llama_data_context() = default;
};

struct llama_data_buffer_context : llama_data_context {
    uint8_t * ptr;
    size_t size_written = 0;

    llama_data_buffer_context(uint8_t * p) : ptr(p) {}

    void write(const void * src, size_t size) override {
        memcpy(ptr, src, size);
        ptr += size;
        size_written += size;
    }

    size_t get_size_written() override {
        return size_written;
    }
};

struct llama_data_file_context : llama_data_context {
    llama_file * file;
    size_t size_written = 0;

    llama_data_file_context(llama_file * f) : file(f) {}

    void write(const void * src, size_t size) override {
        file->write_raw(src, size);
        size_written += size;
    }

    size_t get_size_written() override {
        return size_written;
    }
};

/** copy state data into either a buffer or file depending on the passed in context
 *
 * file context:
 * llama_file file("/path", "wb");
 * llama_data_file_context data_ctx(&file);
 * llama_copy_state_data(ctx, &data_ctx);
 *
 * buffer context:
 * std::vector<uint8_t> buf(max_size, 0);
 * llama_data_buffer_context data_ctx(&buf.data());
 * llama_copy_state_data(ctx, &data_ctx);
 *
*/
static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
    // copy rng
    {
        std::ostringstream rng_ss;
        rng_ss << ctx->rng;

        const std::string & rng_str = rng_ss.str();
        const size_t        rng_size = rng_str.size();

        GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);

        data_ctx->write(&rng_size,      sizeof(rng_size));
        data_ctx->write(rng_str.data(), rng_size);
    }

    // copy logits
    {
        const size_t logits_size = ctx->logits_size;

        data_ctx->write(&logits_size, sizeof(logits_size));

        if (logits_size) {
            data_ctx->write(ctx->logits, logits_size * sizeof(float));
        }
    }

    // copy embeddings
    {
        const size_t embeddings_size = ctx->embd_size;

        data_ctx->write(&embeddings_size, sizeof(embeddings_size));

        if (embeddings_size) {
            data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
        }
    }

    // copy kv cache
    {
        const auto & kv_self = ctx->kv_self;
        const auto & hparams = ctx->model.hparams;

        const uint32_t n_layer      = hparams.n_layer;
        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();

        const size_t   kv_buf_size = kv_self.total_size();
        const uint32_t kv_head     = llama_kv_cache_cell_max(kv_self);
        const uint32_t kv_size     = kv_self.size;
        const uint32_t kv_used     = kv_self.used;

        data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
        data_ctx->write(&kv_head,     sizeof(kv_head));
        data_ctx->write(&kv_size,     sizeof(kv_size));
        data_ctx->write(&kv_used,     sizeof(kv_used));

        if (kv_buf_size) {
            std::vector<uint8_t> tmp_buf;
            for (int il = 0; il < (int) n_layer; ++il) {
                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);

                tmp_buf.resize(k_size);
                ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
                data_ctx->write(tmp_buf.data(), tmp_buf.size());

                if (kv_self.recurrent) {
                    // v is contiguous for recurrent models
                    // TODO: use other tensors for state models than k and v
                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);

                    tmp_buf.resize(v_size);
                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
                    continue;
                }

                // v is not contiguous, copy row by row
                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);

                tmp_buf.resize(v_row_size);
                for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
                }
            }
        }

        for (uint32_t i = 0; i < kv_head; ++i) {
            const auto & cell = kv_self.cells[i];

            const llama_pos pos         = cell.pos;
            const size_t    seq_id_size = cell.seq_id.size();

            data_ctx->write(&pos,         sizeof(pos));
            data_ctx->write(&seq_id_size, sizeof(seq_id_size));

            for (auto seq_id : cell.seq_id) {
                data_ctx->write(&seq_id, sizeof(seq_id));
            }
        }
    }
}

size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
    llama_data_buffer_context data_ctx(dst);
    llama_copy_state_data_internal(ctx, &data_ctx);

    return data_ctx.get_size_written();
}

// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
    const uint8_t * inp = src;

    // set rng
    {
        size_t rng_size;
        memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);

        GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);

        std::string rng_str((const char *)inp, rng_size); inp += rng_size;

        std::istringstream rng_ss(rng_str);
        rng_ss >> ctx->rng;

        GGML_ASSERT(!rng_ss.fail());
    }

    // set logits
    {
        size_t logits_size;

        memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);

        GGML_ASSERT(ctx->logits_size >= logits_size);

        if (logits_size) {
            memcpy(ctx->logits, inp, logits_size * sizeof(float));
            inp += logits_size * sizeof(float);
        }
    }

    // set embeddings
    {
        size_t embeddings_size;

        memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);

        GGML_ASSERT(ctx->embd_size == embeddings_size);

        if (embeddings_size) {
            memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
            inp += embeddings_size * sizeof(float);
        }
    }

    // set kv cache
    {
        const auto & kv_self = ctx->kv_self;
        const auto & hparams = ctx->model.hparams;

        const uint32_t n_layer      = hparams.n_layer;
        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();

        size_t   kv_buf_size;
        uint32_t kv_head;
        uint32_t kv_size;
        uint32_t kv_used;

        memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
        memcpy(&kv_head,     inp, sizeof(kv_head));     inp += sizeof(kv_head);
        memcpy(&kv_size,     inp, sizeof(kv_size));     inp += sizeof(kv_size);
        memcpy(&kv_used,     inp, sizeof(kv_used));     inp += sizeof(kv_used);

        if (kv_buf_size) {
            GGML_ASSERT(kv_self.total_size() == kv_buf_size);

            for (int il = 0; il < (int) n_layer; ++il) {
                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);

                ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
                inp += k_size;

                if (kv_self.recurrent) {
                    // v is contiguous for recurrent models
                    // TODO: use other tensors for state models than k and v
                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);

                    ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
                    inp += v_size;
                    continue;
                }

                // v is not contiguous, copy row by row
                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);

                for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
                    ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
                    inp += v_row_size;
                }
            }
        }

        GGML_ASSERT(kv_self.size == kv_size);

        ctx->kv_self.head = kv_head;
        ctx->kv_self.size = kv_size;
        ctx->kv_self.used = kv_used;

        ctx->kv_self.cells.resize(kv_size);

        for (uint32_t i = 0; i < kv_head; ++i) {
            llama_pos pos;
            size_t    seq_id_size;

            memcpy(&pos,         inp, sizeof(pos));         inp += sizeof(pos);
            memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);

            ctx->kv_self.cells[i].pos = pos;

            llama_seq_id seq_id;

            for (size_t j = 0; j < seq_id_size; ++j) {
                memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
                ctx->kv_self.cells[i].seq_id.insert(seq_id);
            }
        }

        for (uint32_t i = kv_head; i < kv_size; ++i) {
            ctx->kv_self.cells[i].pos = -1;
            ctx->kv_self.cells[i].seq_id.clear();
        }
    }

    const size_t nread    = inp - src;
    const size_t max_size = llama_get_state_size(ctx);

    GGML_ASSERT(nread <= max_size);

    return nread;
}

static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
    llama_file file(path_session, "rb");

    // sanity checks
    {
        const uint32_t magic   = file.read_u32();
        const uint32_t version = file.read_u32();

        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
            LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
            return false;
        }

        llama_hparams session_hparams;
        file.read_raw(&session_hparams, sizeof(llama_hparams));

        if (session_hparams != ctx->model.hparams) {
            LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
            return false;
        }
    }

    // load the prompt
    {
        const uint32_t n_token_count = file.read_u32();

        if (n_token_count > n_token_capacity) {
            LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
            return false;
        }

        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
        *n_token_count_out = n_token_count;
    }

    // restore the context state
    {
        const size_t n_state_size_cur = file.size - file.tell();
        const size_t n_state_size_max = llama_get_state_size(ctx);

        if (n_state_size_cur > n_state_size_max) {
            LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
            return false;
        }

        std::vector<uint8_t> state_data(n_state_size_max);
        file.read_raw(state_data.data(), n_state_size_cur);

        llama_set_state_data(ctx, state_data.data());
    }

    return true;
}

bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
    try {
        return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
        return false;
    }
}

bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
    llama_file file(path_session, "wb");

    file.write_u32(LLAMA_SESSION_MAGIC);
    file.write_u32(LLAMA_SESSION_VERSION);

    file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));

    // save the prompt
    file.write_u32((uint32_t) n_token_count);
    file.write_raw(tokens, sizeof(llama_token) * n_token_count);

    // save the context state using stream saving
    llama_data_file_context data_ctx(&file);
    llama_copy_state_data_internal(ctx, &data_ctx);

    return true;
}

void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
    ctx->cparams.n_threads       = n_threads;
    ctx->cparams.n_threads_batch = n_threads_batch;
}

void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
    ctx->abort_callback      = abort_callback;
    ctx->abort_callback_data = abort_callback_data;
}

void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
    ctx->cparams.causal_attn = causal_attn;
}

struct llama_batch llama_batch_get_one(
             llama_token * tokens,
                 int32_t   n_tokens,
               llama_pos   pos_0,
            llama_seq_id   seq_id) {
    return {
        /*n_tokens       =*/ n_tokens,
        /*tokens         =*/ tokens,
        /*embd           =*/ nullptr,
        /*pos            =*/ nullptr,
        /*n_seq_id       =*/ nullptr,
        /*seq_id         =*/ nullptr,
        /*logits         =*/ nullptr,
        /*all_pos_0      =*/ pos_0,
        /*all_pos_1      =*/ 1,
        /*all_seq_id     =*/ seq_id,
    };
}

struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
    llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };

    if (embd) {
        batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
    } else {
        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
    }

    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens_alloc);
    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens_alloc);
    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
    for (int i = 0; i < n_tokens_alloc; ++i) {
        batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
    }
    batch.seq_id[n_tokens_alloc] = nullptr;

    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens_alloc);

    return batch;
}

void llama_batch_free(struct llama_batch batch) {
    if (batch.token)    free(batch.token);
    if (batch.embd)     free(batch.embd);
    if (batch.pos)      free(batch.pos);
    if (batch.n_seq_id) free(batch.n_seq_id);
    if (batch.seq_id) {
        for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
            free(batch.seq_id[i]);
        }
        free(batch.seq_id);
    }
    if (batch.logits)   free(batch.logits);
}

int32_t llama_decode(
        struct llama_context * ctx,
          struct llama_batch   batch) {
    const int ret = llama_decode_internal(*ctx, batch);
    if (ret < 0) {
        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
    }

    return ret;
}

void llama_synchronize(struct llama_context * ctx) {
    ggml_backend_sched_synchronize(ctx->sched);

    // FIXME: if multiple single tokens are evaluated without a synchronization,
    // the stats will be added to the prompt evaluation stats
    // this should only happen when using batch size 1 to evaluate a batch

    // add the evaluation to the stats
    if (ctx->n_queued_tokens == 1) {
        ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
        ctx->n_eval++;
    } else if (ctx->n_queued_tokens > 1) {
        ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
        ctx->n_p_eval += ctx->n_queued_tokens;
    }

    // get a more accurate load time, upon first eval
    if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
        ctx->has_evaluated_once = true;
    }

    ctx->n_queued_tokens = 0;
    ctx->t_compute_start_us = 0;
}

float * llama_get_logits(struct llama_context * ctx) {
    llama_synchronize(ctx);

    return ctx->logits;
}

float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
    assert(ctx->logits_valid.at(i));

    llama_synchronize(ctx);

    return ctx->logits + i*ctx->model.hparams.n_vocab;
}

float * llama_get_embeddings(struct llama_context * ctx) {
    llama_synchronize(ctx);

    return ctx->embd;
}

float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
    llama_synchronize(ctx);

    return ctx->embd + i*ctx->model.hparams.n_embd;
}

float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
    llama_synchronize(ctx);

    auto it = ctx->embd_seq.find(seq_id);
    if (it == ctx->embd_seq.end()) {
        return nullptr;
    }

    return it->second.data();
}

const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
    return model->vocab.id_to_token[token].text.c_str();
}

float llama_token_get_score(const struct llama_model * model, llama_token token) {
    return model->vocab.id_to_token[token].score;
}

llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
    return model->vocab.id_to_token[token].type;
}

llama_token llama_token_bos(const struct llama_model * model) {
    return model->vocab.special_bos_id;
}

llama_token llama_token_eos(const struct llama_model * model) {
    return model->vocab.special_eos_id;
}

llama_token llama_token_nl(const struct llama_model * model) {
    return model->vocab.linefeed_id;
}

int32_t llama_add_bos_token(const struct llama_model * model) {
    return model->vocab.special_add_bos;
}

int32_t llama_add_eos_token(const struct llama_model * model) {
    return model->vocab.special_add_eos;
}

llama_token llama_token_prefix(const struct llama_model * model) {
    return model->vocab.special_prefix_id;
}

llama_token llama_token_middle(const struct llama_model * model) {
    return model->vocab.special_middle_id;
}

llama_token llama_token_suffix(const struct llama_model * model) {
    return model->vocab.special_suffix_id;
}

llama_token llama_token_eot(const struct llama_model * model) {
    return model->vocab.special_eot_id;
}

int32_t llama_tokenize(
    const struct llama_model * model,
                  const char * text,
                     int32_t   text_len,
                 llama_token * tokens,
                     int32_t   n_tokens_max,
                        bool   add_bos,
                        bool   special) {
    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);

    if (n_tokens_max < (int) res.size()) {
        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
        return -((int) res.size());
    }

    for (size_t i = 0; i < res.size(); i++) {
        tokens[i] = res[i];
    }

    return res.size();
}

static std::string llama_decode_text(const std::string & text) {
    std::string decoded_text;
    auto unicode_sequences = unicode_cpts_from_utf8(text);
    for (auto & unicode_sequence : unicode_sequences) {
        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(unicode_sequence));
    }

    return decoded_text;
}

// does not write null-terminator to buf
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
    if (0 <= token && token < llama_n_vocab(model)) {
        switch (llama_vocab_get_type(model->vocab)) {
        case LLAMA_VOCAB_TYPE_WPM:
        case LLAMA_VOCAB_TYPE_SPM: {
            // NOTE: we accept all unsupported token types,
            // suppressing them like CONTROL tokens.
            if (llama_is_normal_token(model->vocab, token)) {
                std::string result = model->vocab.id_to_token[token].text;
                llama_unescape_whitespace(result);
                if (length < (int) result.length()) {
                    return -(int) result.length();
                }
                memcpy(buf, result.c_str(), result.length());
                return result.length();
            } else if (llama_is_user_defined_token(model->vocab, token)) {
                std::string result = model->vocab.id_to_token[token].text;
                if (length < (int) result.length()) {
                    return -(int) result.length();
                }
                memcpy(buf, result.c_str(), result.length());
                return result.length();
            } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
                if (length < 3) {
                    return -3;
                }
                memcpy(buf, "\xe2\x96\x85", 3);
                return 3;
            } else if (llama_is_control_token(model->vocab, token)) {
                ;
            } else if (llama_is_byte_token(model->vocab, token)) {
                if (length < 1) {
                    return -1;
                }
                buf[0] = llama_token_to_byte(model->vocab, token);
                return 1;
            }
            break;
        }
        case LLAMA_VOCAB_TYPE_BPE: {
            // NOTE: we accept all unsupported token types,
            // suppressing them like CONTROL tokens.
            if (llama_is_normal_token(model->vocab, token)) {
                std::string result = model->vocab.id_to_token[token].text;
                result = llama_decode_text(result);
                if (length < (int) result.length()) {
                    return -(int) result.length();
                }
                memcpy(buf, result.c_str(), result.length());
                return result.length();
            } else if (llama_is_user_defined_token(model->vocab, token)) {
                std::string result = model->vocab.id_to_token[token].text;
                if (length < (int) result.length()) {
                    return -(int) result.length();
                }
                memcpy(buf, result.c_str(), result.length());
                return result.length();
            } else if (llama_is_control_token(model->vocab, token)) {
                ;
            }
            break;
        }
        default:
            GGML_ASSERT(false);
        }
    }
    return 0;
}

// trim whitespace from the beginning and end of a string
static std::string trim(const std::string & str) {
    size_t start = 0;
    size_t end = str.size();
    while (start < end && isspace(str[start])) {
        start += 1;
    }
    while (end > start && isspace(str[end - 1])) {
        end -= 1;
    }
    return str.substr(start, end - start);
}

// Simple version of "llama_apply_chat_template" that only works with strings
// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
static int32_t llama_chat_apply_template_internal(
    const std::string & tmpl,
    const std::vector<const llama_chat_message *> & chat,
    std::string & dest, bool add_ass) {
    // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
    std::stringstream ss;
    if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
        // chatml template
        for (auto message : chat) {
            ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
        }
        if (add_ass) {
            ss << "<|im_start|>assistant\n";
        }
    } else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
        // llama2 template and its variants
        // [variant] support system message
        bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
        // [variant] space before + after response
        bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
        // [variant] add BOS inside history
        bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos;
        // [variant] trim spaces from the input message
        bool strip_message = tmpl.find("content.strip()") != std::string::npos;
        // construct the prompt
        bool is_inside_turn = true; // skip BOS at the beginning
        ss << "[INST] ";
        for (auto message : chat) {
            std::string content = strip_message ? trim(message->content) : message->content;
            std::string role(message->role);
            if (!is_inside_turn) {
                is_inside_turn = true;
                ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
            }
            if (role == "system") {
                if (support_system_message) {
                    ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
                } else {
                    // if the model does not support system message, we still include it in the first message, but without <<SYS>>
                    ss << content << "\n";
                }
            } else if (role == "user") {
                ss << content << " [/INST]";
            } else {
                ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << "</s>";
                is_inside_turn = false;
            }
        }
        // llama2 templates seem to not care about "add_generation_prompt"
    } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
        // zephyr template
        for (auto message : chat) {
            ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
        }
        if (add_ass) {
            ss << "<|assistant|>\n";
        }
    } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
        // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
        for (auto message : chat) {
            std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
            ss << bos << message->role << "\n" << message->content << "</s>\n";
        }
        if (add_ass) {
            ss << "<s>assistant\n";
        }
    } else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
        // google/gemma-7b-it
        std::string system_prompt = "";
        for (auto message : chat) {
            std::string role(message->role);
            if (role == "system") {
                // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
                system_prompt = trim(message->content);
                continue;
            }
            // in gemma, "assistant" is "model"
            role = role == "assistant" ? "model" : message->role;
            ss << "<start_of_turn>" << role << "\n";
            if (!system_prompt.empty() && role != "model") {
                ss << system_prompt << "\n\n";
                system_prompt = "";
            }
            ss << trim(message->content) << "<end_of_turn>\n";
        }
        if (add_ass) {
            ss << "<start_of_turn>model\n";
        }
    } else {
        // template not supported
        return -1;
    }
    dest = ss.str();
    return dest.size();
}

LLAMA_API int32_t llama_chat_apply_template(
                const struct llama_model * model,
                              const char * tmpl,
         const struct llama_chat_message * chat,
                                  size_t   n_msg,
                                    bool   add_ass,
                                    char * buf,
                                 int32_t   length) {
    std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
    if (tmpl == nullptr) {
        GGML_ASSERT(model != nullptr);
        // load template from model
        std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
        std::string template_key = "tokenizer.chat_template";
        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
        if (res < 0) {
            // worst case: there is no information about template, we will use chatml by default
            curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
        } else {
            curr_tmpl = std::string(model_template.data(), model_template.size());
        }
    }

    // format the chat to string
    std::vector<const llama_chat_message *> chat_vec;
    chat_vec.resize(n_msg);
    for (size_t i = 0; i < n_msg; i++) {
        chat_vec[i] = &chat[i];
    }

    std::string formatted_chat;
    int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
    if (res < 0) {
        return res;
    }
    if (buf && length > 0) {
        strncpy(buf, formatted_chat.c_str(), length);
    }
    return res;
}

struct llama_timings llama_get_timings(struct llama_context * ctx) {
    struct llama_timings result = {
        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,

        /*.n_sample =*/ std::max(1, ctx->n_sample),
        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
        /*.n_eval   =*/ std::max(1, ctx->n_eval),
    };

    return result;
}

void llama_print_timings(struct llama_context * ctx) {
    const llama_timings timings = llama_get_timings(ctx);

    LLAMA_LOG_INFO("\n");
    LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, timings.t_load_ms);
    LLAMA_LOG_INFO("%s:      sample time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
    LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
    LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
    LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (timings.t_end_ms - timings.t_start_ms), (timings.n_p_eval + timings.n_eval));
}

void llama_reset_timings(struct llama_context * ctx) {
    ctx->t_start_us = ggml_time_us();
    ctx->t_sample_us = ctx->n_sample = 0;
    ctx->t_eval_us   = ctx->n_eval   = 0;
    ctx->t_p_eval_us = ctx->n_p_eval = 0;
}

const char * llama_print_system_info(void) {
    static std::string s;

    s  = "";
    s += "AVX = "         + std::to_string(ggml_cpu_has_avx())         + " | ";
    s += "AVX_VNNI = "    + std::to_string(ggml_cpu_has_avx_vnni())    + " | ";
    s += "AVX2 = "        + std::to_string(ggml_cpu_has_avx2())        + " | ";
    s += "AVX512 = "      + std::to_string(ggml_cpu_has_avx512())      + " | ";
    s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
    s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
    s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
    s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
    s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
    s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
    s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
    s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
    s += "BLAS = "        + std::to_string(ggml_cpu_has_blas())        + " | ";
    s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
    s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
    s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
    s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";

    return s.c_str();
}

void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
    fprintf(stream, "\n");
    fprintf(stream, "###########\n");
    fprintf(stream, "# Timings #\n");
    fprintf(stream, "###########\n");
    fprintf(stream, "\n");

    fprintf(stream, "mst_eval: %.2f  # ms / token during generation\n",
            1.0e-3 * ctx->t_eval_us / ctx->n_eval);
    fprintf(stream, "mst_p_eval: %.2f  # ms / token during prompt processing\n",
            1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
    fprintf(stream, "mst_sample: %.2f  # ms / token during sampling\n",
            1.0e-3 * ctx->t_sample_us / ctx->n_sample);
    fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
    fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
    fprintf(stream, "t_eval_us: %" PRId64 "  # total microseconds spent generating tokens\n", ctx->t_eval_us);
    fprintf(stream, "t_load_us: %" PRId64 "  # total microseconds spent loading the model\n", ctx->t_load_us);
    fprintf(stream, "t_p_eval_us: %" PRId64 "  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->t_sample_us);
    fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
            1.0e6 * ctx->n_eval / ctx->t_eval_us);
    fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
            1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
    fprintf(stream, "ts_sample: %.2f  # tokens / second during sampling\n",
            1.0e6 * ctx->n_sample / ctx->t_sample_us);
}

// For internal test use
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
    struct llama_context * ctx
) {
    return ctx->model.tensors_by_name;
}

void llama_log_set(ggml_log_callback log_callback, void * user_data) {
    g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
    g_state.log_callback_user_data = user_data;
#ifdef GGML_USE_METAL
    ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
#endif
}

static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
    va_list args_copy;
    va_copy(args_copy, args);
    char buffer[128];
    int len = vsnprintf(buffer, 128, format, args);
    if (len < 128) {
        g_state.log_callback(level, buffer, g_state.log_callback_user_data);
    } else {
        char* buffer2 = new char[len+1];
        vsnprintf(buffer2, len+1, format, args_copy);
        buffer2[len] = 0;
        g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
        delete[] buffer2;
    }
    va_end(args_copy);
}

static void llama_log_internal(ggml_log_level level, const char * format, ...) {
    va_list args;
    va_start(args, format);
    llama_log_internal_v(level, format, args);
    va_end(args);
}

static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
    (void) level;
    (void) user_data;
    fputs(text, stderr);
    fflush(stderr);
}