char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

Xuan-Son Nguyenquantize: row-slab stream to avoid thread starvation (#27830)1b89a43e32026-09-01

#include "llama-impl.h"
#include "llama-model.h"
#include "llama-model-loader.h"
#include "llama-ext.h"
#include "llama.h"

#include <algorithm>
#include <cmath>
#include <cstring>
#include <cinttypes>
#include <fstream>
#include <mutex>
#include <regex>
#include <thread>
#include <unordered_map>

// result of parsing --tensor-type option
// (changes to this struct must be reflected in tools/quantize/quantize.cpp)
struct tensor_type_option {
    std::string name;
    ggml_type type = GGML_TYPE_COUNT;
};

// tensor categorization - used to avoid repeated string matching in quantization logic.
// this is different from LLM_TN - we want broad categories, not specific tensor names per arch.
enum class tensor_category {
    TOKEN_EMBD,
    ATTENTION_Q,
    ATTENTION_V,
    ATTENTION_K,
    ATTENTION_QKV,
    ATTENTION_KV_B,
    ATTENTION_OUTPUT,
    FFN_UP,
    FFN_GATE,
    FFN_DOWN,
    OUTPUT,
    OTHER
};

// max amount of tensor data kept in memory while quantizing a single tensor
static const size_t LLAMA_QUANT_MAX_BUF_SIZE = 8ull*1024*1024*1024;

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

static std::string remap_layer(const std::string & orig_name, const std::vector<int> & prune, std::map<int, std::string> & mapped, int & next_id) {
    if (prune.empty()) {
        return orig_name;
    }

    static const std::regex pattern(R"(blk\.(\d+)\.)");
    if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
        const int blk = std::stoi(match[1]);
        std::string new_name = orig_name;

        if (mapped.count(blk)) {
            // Already mapped, do nothing
        } else if (std::find(prune.begin(), prune.end(), blk) != prune.end()) {
            mapped[blk] = "";
        } else if (blk < prune.front()) {
            mapped[blk] = std::to_string(blk);
            next_id = blk + 1;
        } else {
            mapped[blk] = std::to_string(next_id);
            ++next_id;
        }

        return mapped[blk].empty() ? mapped[blk] : new_name.replace(match.position(1), match.length(1), mapped[blk]);
    }

    return orig_name;
}

static std::string remap_imatrix(const std::string & orig_name, const std::map<int, std::string> & mapped) {
    if (mapped.empty()) {
        return orig_name;
    }

    static const std::regex pattern(R"(blk\.(\d+)\.)");
    if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
        const std::string blk(match[1]);
        std::string new_name = orig_name;

        for (const auto & p : mapped) {
            if (p.second == blk) {
                return new_name.replace(match.position(1), match.length(1), std::to_string(p.first));
            }
        }
        GGML_ABORT("\n%s: imatrix mapping error for %s\n", __func__, orig_name.c_str());
    }

    return orig_name;
}

//
// helper functions for tensor name matching
//

static bool tensor_name_match_token_embd(const char * tensor_name) {
    return std::strcmp(tensor_name, "token_embd.weight") == 0 ||
           std::strcmp(tensor_name, "per_layer_token_embd.weight") == 0;
}

static bool tensor_name_match_output_weight(const char * tensor_name) {
    return std::strcmp(tensor_name, "output.weight") == 0;
}

//
// tensor categorization for quantization
//
// (this is different from LLM_TN - we want broad categories, not specific tensor names per arch)
//

static tensor_category tensor_get_category(const std::string & tensor_name) {
    if (tensor_name_match_output_weight(tensor_name.c_str())) {
        return tensor_category::OUTPUT;
    }
    if (tensor_name_match_token_embd(tensor_name.c_str())) {
        return tensor_category::TOKEN_EMBD;
    }
    if (tensor_name.find("attn_qkv.weight") != std::string::npos) {
        return tensor_category::ATTENTION_QKV;
    }
    if (tensor_name.find("attn_kv_b.weight") != std::string::npos) {
        return tensor_category::ATTENTION_KV_B;
    }
    if (tensor_name.find("attn_v.weight") != std::string::npos) {
        return tensor_category::ATTENTION_V;
    }
    if (tensor_name.find("attn_k.weight") != std::string::npos) {
        return tensor_category::ATTENTION_K;
    }
    if (tensor_name.find("attn_q.weight") != std::string::npos) {
        return tensor_category::ATTENTION_Q;
    }
    if (tensor_name.find("attn_output.weight") != std::string::npos) {
        return tensor_category::ATTENTION_OUTPUT;
    }
    if (tensor_name.find("ffn_up") != std::string::npos) {
        return tensor_category::FFN_UP;
    }
    if (tensor_name.find("ffn_gate") != std::string::npos) {
        return tensor_category::FFN_GATE;
    }
    if (tensor_name.find("ffn_down") != std::string::npos) {
        return tensor_category::FFN_DOWN;
    }
    return tensor_category::OTHER;
}

// check if category is for attention-v-like tensors (more sensitive to quantization)
static bool category_is_attn_v(tensor_category cat) {
    return cat == tensor_category::ATTENTION_V     ||
           cat == tensor_category::ATTENTION_QKV   ||
           cat == tensor_category::ATTENTION_KV_B;
}

//
// quantization state
//

struct quantize_state_impl {
    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_fallback    = 0;

    bool has_imatrix = false;

    // used to figure out if a model has tied embeddings (tok_embd shares weights with output)
    bool has_tied_embeddings = true; // assume tied until we see output.weight

    // tensor type override patterns (compiled once, used twice)
    std::vector<std::pair<std::regex, ggml_type>> tensor_type_patterns;

    quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params):
        model(model), params(params)
    {
        // compile regex patterns once - they are expensive
        if (params->tt_overrides) {
            for (const auto * p = params->tt_overrides; p->pattern != nullptr; p++) {
                tensor_type_patterns.emplace_back(std::regex(p->pattern), p->type);
            }
        }
    }
};

// per-tensor metadata, computed in the preliminary loop and used in the main loop
struct tensor_metadata {
    std::string     name;
    ggml_type       target_type;
    tensor_category category;
    std::string     remapped_imatrix_name;
    bool            allows_quantization;
    bool            requires_imatrix;
};

//
// dequantization
//

static void llama_tensor_dequantize_impl(
    ggml_type type, const void * data, float * f32_output, std::vector<std::thread> & workers,
    const size_t nelements, const int nthread
) {
    const ggml_type_traits * qtype = ggml_get_type_traits(type);
    if (ggml_is_quantized(type)) {
        if (qtype->to_float == NULL) {
            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(type)));
        }
    } else if (type != GGML_TYPE_F16 &&
               type != GGML_TYPE_BF16) {
        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(type)));
    }

    if (nthread < 2) {
        if (type == GGML_TYPE_F16) {
            ggml_fp16_to_fp32_row((const ggml_fp16_t *)data, f32_output, nelements);
        } else if (type == GGML_TYPE_BF16) {
            ggml_bf16_to_fp32_row((const ggml_bf16_t *)data, f32_output, nelements);
        } else if (ggml_is_quantized(type)) {
            qtype->to_float(data, f32_output, nelements);
        } else {
            GGML_ABORT("fatal error"); // unreachable
        }
        return;
    }

    size_t block_size;
    if (type == GGML_TYPE_F16 ||
        type == GGML_TYPE_BF16) {
        block_size = 1;
    } else {
        block_size = (size_t)ggml_blck_size(type);
    }

    size_t block_size_bytes = ggml_type_size(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, const uint8_t * inbuf, float * outbuf, int nels) {
            if (typ == GGML_TYPE_F16) {
                ggml_fp16_to_fp32_row((const ggml_fp16_t *)inbuf, outbuf, nels);
            } else if (typ == GGML_TYPE_BF16) {
                ggml_bf16_to_fp32_row((const ggml_bf16_t *)inbuf, outbuf, nels);
            } else {
                qtype->to_float(inbuf, outbuf, nels);
            }
        };
        workers.emplace_back(compute, type, (const uint8_t *) 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();
}

//
// do we allow this tensor to be quantized?
//

static bool tensor_allows_quantization(const llama_model_quantize_params * params, llm_arch arch, const ggml_tensor * tensor) {
    // trivial checks first -- no string ops needed
    if (params->only_copy)       return false;

    // quantize only 2D and 3D tensors (experts)
    if (ggml_n_dims(tensor) < 2) return false;

    const std::string name = ggml_get_name(tensor);

    // 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'?

    // do not quantize norm tensors
    quantize &= name.find("_norm.weight") == std::string::npos;

    quantize &= params->quantize_output_tensor || name != "output.weight";

    // 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 the i32 token-id -> expert-id routing table (DeepSeek-V4)
    quantize &= name.find("ffn_gate_tid2eid.weight") == std::string::npos;

    // these are very small (e.g. 4x4)
    quantize &= name.find("altup")  == std::string::npos;
    quantize &= name.find("laurel") == std::string::npos;

    // these are not too big so keep them as it is
    quantize &= name.find("per_layer_model_proj") == std::string::npos;

    // do not quantize positional embeddings and token types (BERT)
    quantize &= name != LLM_TN(arch)(LLM_TENSOR_POS_EMBD,    "weight");
    quantize &= name != LLM_TN(arch)(LLM_TENSOR_TOKEN_TYPES, "weight");

    // do not quantize Mamba/Kimi's small conv1d weights
    // NOTE: can't use LLM_TN here because the layer number is not known
    quantize &= name.find("ssm_conv1d") == std::string::npos;
    quantize &= name.find("shortconv.conv.weight") == std::string::npos;

    // do not quantize MiniMax's indexer projection weights, they are tiny
    quantize &= name.find("indexer.k_proj.weight") == std::string::npos;
    quantize &= name.find("indexer.q_proj.weight") == std::string::npos;

    // do not quantize RWKV's small yet 2D weights
    quantize &= name.find("time_mix_first.weight") == std::string::npos;
    quantize &= name.find("time_mix_w0.weight") == std::string::npos;
    quantize &= name.find("time_mix_w1.weight") == std::string::npos;
    quantize &= name.find("time_mix_w2.weight") == std::string::npos;
    quantize &= name.find("time_mix_v0.weight") == std::string::npos;
    quantize &= name.find("time_mix_v1.weight") == std::string::npos;
    quantize &= name.find("time_mix_v2.weight") == std::string::npos;
    quantize &= name.find("time_mix_a0.weight") == std::string::npos;
    quantize &= name.find("time_mix_a1.weight") == std::string::npos;
    quantize &= name.find("time_mix_a2.weight") == std::string::npos;
    quantize &= name.find("time_mix_g1.weight") == std::string::npos;
    quantize &= name.find("time_mix_g2.weight") == std::string::npos;
    quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos;
    quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos;
    quantize &= name.find("time_mix_lerp_fused.weight") == std::string::npos;

    // do not quantize relative position bias (T5)
    quantize &= name.find("attn_rel_b.weight") == std::string::npos;

    // do not quantize specific multimodal tensors
    quantize &= name.find(".position_embd") == std::string::npos;
    quantize &= name.find("sam.pos_embd")   == std::string::npos;
    quantize &= name.find("sam.neck.")      == std::string::npos;
    quantize &= name.find("sam.net_")       == std::string::npos;
    quantize &= name.find(".rel_pos")       == std::string::npos;
    quantize &= name.find(".patch_embd")    == std::string::npos;
    quantize &= name.find(".patch_merger")  == std::string::npos;

    // audio codebook
    quantize &= name.find("a.rvq.codebook")  == std::string::npos;
    quantize &= name.find("mm.a.code_embd")  == std::string::npos;

    return quantize;
}

//
// tensor type selection
//

// incompatible tensor shapes are handled here - fallback to a compatible type
static ggml_type tensor_type_fallback(quantize_state_impl & qs, const ggml_tensor * t, const ggml_type target_type) {
    ggml_type return_type = target_type;

    const int64_t ncols = t->ne[0];
    const int64_t qk_k = ggml_blck_size(target_type);

    if (ncols % qk_k != 0) { // this tensor's shape is incompatible with this quant
        LLAMA_LOG_WARN("warning: %-36s - ncols %6" PRId64 " not divisible by %3" PRId64 " (required for type %7s) ",
                        t->name, ncols, qk_k, ggml_type_name(target_type));
        ++qs.n_fallback;

        switch (target_type) {
            // types on the left: block size 256
            case GGML_TYPE_IQ1_S:
            case GGML_TYPE_IQ1_M:
            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:   // types on the right: block size 32
            case GGML_TYPE_IQ4_XS:  return_type = GGML_TYPE_IQ4_NL; break;
            case GGML_TYPE_Q2_0:
            case GGML_TYPE_Q2_K:
            case GGML_TYPE_Q3_K:
            case GGML_TYPE_TQ1_0:
            case GGML_TYPE_TQ2_0:   return_type = GGML_TYPE_Q4_0;   break;
            case GGML_TYPE_Q4_K:    return_type = GGML_TYPE_Q5_0;   break;
            case GGML_TYPE_Q5_K:    return_type = GGML_TYPE_Q5_1;   break;
            case GGML_TYPE_Q6_K:    return_type = GGML_TYPE_Q8_0;   break;
            default:
                if (qk_k <= 32) {
                    // the target is already a 32-block type, so there is no smaller block to demote to
                    // the check below turns it into F16, as a 256-block type does when its fallback does not fit
                    return_type = target_type;
                    break;
                }
                throw std::runtime_error(format("no tensor type fallback is defined for type %s",
                                                ggml_type_name(target_type)));
        }
        if (ncols % ggml_blck_size(return_type) != 0) {
            //
            // the fallback return type is still not compatible for this tensor!
            //
            // most likely, this tensor's first dimension is not divisible by 32.
            // this is very rare. we can either abort the quantization, or
            // fallback to F16 / F32.
            //
            LLAMA_LOG_WARN("(WARNING: must use F16 due to unusual shape) ");
            return_type = GGML_TYPE_F16;
        }
        LLAMA_LOG_WARN("-> falling back to %7s\n", ggml_type_name(return_type));
    }
    return return_type;
}

// internal standard logic for selecting the target tensor type based on tensor category, ftype, and model arch
static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype, tensor_category category) {
    const std::string name = ggml_get_name(tensor);

    // TODO: avoid hardcoded tensor names - use the TN_* constants
    const llm_arch arch = qs.model.arch;

    auto use_more_bits = [](int i_layer, int n_layers) -> bool {
        return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_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 occasionally 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.
            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 (category == tensor_category::OUTPUT || (qs.has_tied_embeddings && category == tensor_category::TOKEN_EMBD)) {
        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
            new_type = qs.params->output_tensor_type;
        } else {
            const int64_t nx = tensor->ne[0];
            const int64_t qk_k = ggml_blck_size(new_type);

            if (ftype == LLAMA_FTYPE_MOSTLY_MXFP4_MOE) {
                new_type = GGML_TYPE_Q8_0;
            }
            else 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   ||
                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
                new_type = GGML_TYPE_Q5_K;
            }
            else if (new_type != GGML_TYPE_Q8_0) {
                new_type = GGML_TYPE_Q6_K;
            }
        }
    } else if (ftype == LLAMA_FTYPE_MOSTLY_MXFP4_MOE) {
        // MoE   tensors -> MXFP4
        // other tensors -> Q8_0
        // MLA projection tensors are also 3D, so match expert tensor roles explicitly.
        const bool is_bailingmoe3_expert = arch == LLM_ARCH_BAILINGMOE3 &&
            (category == tensor_category::FFN_UP ||
             category == tensor_category::FFN_GATE ||
             category == tensor_category::FFN_DOWN);
        if (tensor->ne[2] > 1 && (arch != LLM_ARCH_BAILINGMOE3 || is_bailingmoe3_expert)) {
            new_type = GGML_TYPE_MXFP4;
        } else {
            new_type = GGML_TYPE_Q8_0;
        }
    } else if (category == tensor_category::TOKEN_EMBD) {
        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
            new_type = qs.params->token_embedding_type;
        } else {
            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
                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_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0 || ftype == LLAMA_FTYPE_MOSTLY_Q2_0) {
                new_type = GGML_TYPE_Q4_K;
            }
        }
    } 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    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
        if (category_is_attn_v(category)) {
            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 && category == tensor_category::ATTENTION_K) {
            new_type = GGML_TYPE_Q4_K;
        }
        else if (category == tensor_category::FFN_DOWN) {
            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 (category == tensor_category::ATTENTION_OUTPUT) {
            if (qs.model.hparams.n_expert == 8) {
                new_type = GGML_TYPE_Q5_K;
            } else {
                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) 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 (category_is_attn_v(category)) {
        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_XS || 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;
        if (qs.model.type == LLM_TYPE_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 (category == tensor_category::ATTENTION_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;
        }
        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 (category == tensor_category::ATTENTION_Q) {
        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 (category == tensor_category::FFN_DOWN) {
        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 (category == tensor_category::ATTENTION_OUTPUT) {
        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 (category == tensor_category::ATTENTION_QKV) {
        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 (category == tensor_category::FFN_GATE) {
        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 (category == tensor_category::FFN_UP) {
        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;
    }

    return new_type;
}

// outer wrapper: determine the ggml_type that this tensor should be quantized to
static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_model_quantize_params * params, const ggml_tensor * tensor, ggml_type default_type, const tensor_metadata & tm) {
    if (!tensor_allows_quantization(params, qs.model.arch, tensor)) {
        return tensor->type;
    }
    if (params->token_embedding_type < GGML_TYPE_COUNT && tm.category == tensor_category::TOKEN_EMBD) {
        // per_layer_token_embd follows --token-embedding-type by default, but it is a large
        // separate table, so let an explicit --tensor-type name it
        bool named = false;
        if (std::strcmp(tensor->name, "per_layer_token_embd.weight") == 0) {
            const std::string tensor_name(tensor->name);
            for (const auto & [pattern, qtype] : qs.tensor_type_patterns) {
                if (std::regex_search(tensor_name, pattern)) {
                    named = true;
                    break;
                }
            }
        }
        if (!named) {
            return params->token_embedding_type;
        }
    }
    if (params->output_tensor_type < GGML_TYPE_COUNT && tm.category == tensor_category::OUTPUT) {
        return params->output_tensor_type;
    }

    ggml_type new_type = default_type;

    // get more optimal quantization type based on the tensor shape, layer, etc.
    if (ggml_is_quantized(default_type)) {
        // if the user provided tensor types - use those
        bool manual = false;
        if (!qs.tensor_type_patterns.empty()) {
            const std::string tensor_name(tensor->name);
            for (const auto & [pattern, qtype] : qs.tensor_type_patterns) {
                if (std::regex_search(tensor_name, pattern)) {
                    if (qtype != new_type) {
                        LLAMA_LOG_WARN("%s: %-36s - applying manual override: %s -> %s\n",
                                       __func__, tensor_name.c_str(), ggml_type_name(new_type), ggml_type_name(qtype));
                        new_type = qtype;
                    }
                    manual = true;
                    break;
                }
            }
        }

        // if not manual - use the standard logic for choosing the quantization type based on the selected mixture
        if (!manual && !params->pure) {
            new_type = llama_tensor_get_type_impl(qs, new_type, tensor, params->ftype, tm.category);
        }

        // incompatible tensor shapes are handled here - fallback to a compatible type
        new_type = tensor_type_fallback(qs, tensor, new_type);
    }

    return new_type;
}

//
// quantization implementation
//

// quantize rows [first_row, first_row + nrows), indexed globally across all expert matrices
// note: chunks never cross an expert boundary since each expert has its own imatrix slice
static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t first_row, int64_t nrows, int64_t nrows_per_expert, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
    const size_t row_size = ggml_row_size(new_type, n_per_row);

    auto imatrix_for_row = [=](int64_t row_global) {
        return imatrix ? imatrix + (row_global / nrows_per_expert) * n_per_row : nullptr;
    };

    if (nthread < 2) {
        // single-thread
        size_t new_size = 0;
        for (int64_t row = 0; row < nrows;) {
            const int64_t row_global = first_row + row;
            const int64_t this_nrow  = std::min(nrows - row, nrows_per_expert - row_global % nrows_per_expert);
            void * this_data = (char *) new_data + row * row_size;
            size_t this_size = ggml_quantize_chunk(new_type, f32_data + row * n_per_row, this_data, 0, this_nrow, n_per_row, imatrix_for_row(row_global));
            if (!ggml_validate_row_data(new_type, this_data, this_size)) {
                throw std::runtime_error("quantized data validation failed");
            }
            new_size += this_size;
            row += this_nrow;
        }
        return new_size;
    }

    std::mutex mutex;
    int64_t counter = 0;
    size_t new_size = 0;
    bool valid = true;
    auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
            first_row, nrows, nrows_per_expert, n_per_row, row_size, imatrix_for_row]() {
        const int64_t nrows_per_chunk = chunk_size / n_per_row;
        size_t local_size = 0;
        while (true) {
            std::unique_lock<std::mutex> lock(mutex);
            if (counter >= nrows) {
                if (local_size > 0) {
                    new_size += local_size;
                }
                break;
            }
            const int64_t row        = counter;
            const int64_t row_global = first_row + row;
            // stop at the expert boundary
            const int64_t this_nrow  = std::min(std::min(nrows - row, nrows_per_chunk), nrows_per_expert - row_global % nrows_per_expert);
            counter += this_nrow;
            lock.unlock();

            void * this_data = (char *) new_data + row * row_size;
            size_t this_size = ggml_quantize_chunk(new_type, f32_data + row * n_per_row, this_data, 0, this_nrow, n_per_row, imatrix_for_row(row_global));
            local_size += this_size;

            // validate the quantized data
            if (!ggml_validate_row_data(new_type, this_data, this_size)) {
                std::unique_lock<std::mutex> lock(mutex);
                valid = false;
                break;
            }
        }
    };
    for (int it = 0; it < nthread - 1; ++it) {
        workers.emplace_back(compute);
    }
    compute();
    for (auto & w : workers) { w.join(); }
    workers.clear();
    if (!valid) {
        throw std::runtime_error("quantized data validation failed");
    }
    return new_size;
}

//
// imatrix requirement check
//

static bool tensor_requires_imatrix(const char * tensor_name, const ggml_type dst_type, const llama_ftype ftype) {
    if (tensor_name_match_token_embd(tensor_name) || tensor_name_match_output_weight(tensor_name)) {
        return false;
    }
    switch (dst_type) {
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ1_M:
        case GGML_TYPE_IQ1_S:
            return true;
        case GGML_TYPE_Q2_K:
            // as a general rule, the k-type quantizations don't require imatrix data.
            // the only exception is Q2_K tensors that are part of a Q2_K_S file.
            return ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S;
        default:
            return false;
    }
}

//
// given a file type, get the default tensor type
//

ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
    switch (ftype) {
        case LLAMA_FTYPE_MOSTLY_Q4_0: return GGML_TYPE_Q4_0;
        case LLAMA_FTYPE_MOSTLY_Q4_1: return GGML_TYPE_Q4_1;
        case LLAMA_FTYPE_MOSTLY_Q5_0: return GGML_TYPE_Q5_0;
        case LLAMA_FTYPE_MOSTLY_Q5_1: return GGML_TYPE_Q5_1;
        case LLAMA_FTYPE_MOSTLY_Q8_0: return GGML_TYPE_Q8_0;
        case LLAMA_FTYPE_MOSTLY_F16:  return GGML_TYPE_F16;
        case LLAMA_FTYPE_MOSTLY_BF16: return GGML_TYPE_BF16;
        case LLAMA_FTYPE_ALL_F32:     return GGML_TYPE_F32;
        case LLAMA_FTYPE_MOSTLY_Q1_0: return GGML_TYPE_Q1_0;
        case LLAMA_FTYPE_MOSTLY_Q2_0: return GGML_TYPE_Q2_0;

        case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: return GGML_TYPE_MXFP4;

        // K-quants
        case LLAMA_FTYPE_MOSTLY_Q2_K_S:
        case LLAMA_FTYPE_MOSTLY_Q2_K:    return GGML_TYPE_Q2_K;
        case LLAMA_FTYPE_MOSTLY_IQ3_XS:  return GGML_TYPE_IQ3_S;
        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  return GGML_TYPE_Q3_K;
        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  return GGML_TYPE_Q4_K;
        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  return GGML_TYPE_Q5_K;
        case LLAMA_FTYPE_MOSTLY_Q6_K:    return GGML_TYPE_Q6_K;
        case LLAMA_FTYPE_MOSTLY_TQ1_0:   return GGML_TYPE_TQ1_0;
        case LLAMA_FTYPE_MOSTLY_TQ2_0:   return GGML_TYPE_TQ2_0;
        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: return GGML_TYPE_IQ2_XXS;
        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  return GGML_TYPE_IQ2_XS;
        case LLAMA_FTYPE_MOSTLY_IQ2_S:   return GGML_TYPE_IQ2_XS;
        case LLAMA_FTYPE_MOSTLY_IQ2_M:   return GGML_TYPE_IQ2_S;
        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return GGML_TYPE_IQ3_XXS;
        case LLAMA_FTYPE_MOSTLY_IQ1_S:   return GGML_TYPE_IQ1_S;
        case LLAMA_FTYPE_MOSTLY_IQ1_M:   return GGML_TYPE_IQ1_M;
        case LLAMA_FTYPE_MOSTLY_IQ4_NL:  return GGML_TYPE_IQ4_NL;
        case LLAMA_FTYPE_MOSTLY_IQ4_XS:  return GGML_TYPE_IQ4_XS;
        case LLAMA_FTYPE_MOSTLY_IQ3_S:
        case LLAMA_FTYPE_MOSTLY_IQ3_M:   return GGML_TYPE_IQ3_S;

        default: return GGML_TYPE_COUNT;
    }
}


static void init_quantize_state_counters(quantize_state_impl & qs, std::vector<tensor_metadata> & metadata) {
    for (auto & tm : metadata) {
        tensor_category cat = tensor_get_category(tm.name);
        tm.category = cat;

        if (category_is_attn_v(cat)) {
            ++qs.n_attention_wv;
        }

        if (cat == tensor_category::OUTPUT) {
            qs.has_tied_embeddings = false;
        }
    }
    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer_all;
}

//
// main quantization driver
//

static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
    llama_ftype ftype = params->ftype;

    int nthread = params->nthread;

    if (nthread <= 0) {
        nthread = std::thread::hardware_concurrency();
    }

    ggml_type default_type = llama_ftype_get_default_type(ftype);
    if (default_type == GGML_TYPE_COUNT) {
        throw std::runtime_error(format("invalid output file type %d\n", ftype));
    }

    // mmap consistently increases speed on 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 llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP;
#else
    constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_NONE;
#endif

    const llama_model_kv_override * kv_overrides = params->kv_overrides;
    std::vector<std::string> splits = {};
    llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr,
        fname_inp, splits, /*file*/ nullptr, /*load_mode*/ load_mode, /*check_tensors*/ true, /*no_alloc*/ false, /*load_mtp*/ true, kv_overrides, nullptr);
    ml.init_mappings(false); // no prefetching

    auto mparams = llama_model_default_params();
    std::unique_ptr<llama_model> model_ptr(llama_model_create(ml, mparams));

    auto * model = dynamic_cast<llama_model_base *>(model_ptr.get());
    if (model == nullptr) {
        GGML_ABORT("fatal error: model does not implement llama_model_base");
    }

    model->load_hparams(ml);
    model->load_stats  (ml);

    quantize_state_impl qs(*model, params);

    if (params->only_copy) {
        ftype = ml.ftype;
    }
    std::unordered_map<std::string, std::vector<float>> i_data;
    const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
    if (params->imatrix) {
        for (const llama_model_imatrix_data * p = params->imatrix; p->name != nullptr; p++) {
            i_data.emplace(p->name, std::vector<float>(p->data, p->data + p->size));
        }
        imatrix_data = & i_data;
        if (imatrix_data) {
            LLAMA_LOG_INFO("\n%s: have importance matrix data with %d entries\n",
                           __func__, (int)imatrix_data->size());
            qs.has_imatrix = true;
            // check imatrix for nans or infs
            for (const auto & kv : *imatrix_data) {
                for (float f : kv.second) {
                    if (!std::isfinite(f)) {
                        throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
                    }
                }
            }
        }
    }

    const size_t align = GGUF_DEFAULT_ALIGNMENT;
    gguf_context_ptr ctx_out { gguf_init_empty() };

    std::vector<int> prune_list = {};
    if (params->prune_layers) {
        for (const int32_t * p = params->prune_layers; * p != -1; p++) {
            prune_list.push_back(* p);
        }
    }

    // copy the KV pairs from the input file
    gguf_set_kv     (ctx_out.get(), ml.metadata);
    gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION).c_str(), GGML_QNT_VERSION);
    gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_GENERAL_FILE_TYPE).c_str(), ftype);

    // Remove split metadata
    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
    gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());

    if (params->kv_overrides) {
        for (const llama_model_kv_override * o = params->kv_overrides; o->key[0] != 0; ++o) {
            if (o->tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
                gguf_set_val_f32(ctx_out.get(), o->key, o->val_f64);
            } else if (o->tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
                // Setting type to UINT32. See https://github.com/ggml-org/llama.cpp/pull/14182 for context
                gguf_set_val_u32(ctx_out.get(), o->key, (uint32_t)std::abs(o->val_i64));
            } else if (o->tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
                gguf_set_val_bool(ctx_out.get(), o->key, o->val_bool);
            } else if (o->tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
                gguf_set_val_str(ctx_out.get(), o->key, o->val_str);
            } else {
                LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o->key);
            }
        }
    }

    std::map<int, std::string> mapped;
    int blk_id = 0;

    // make a list of weights
    std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
    tensors.reserve(ml.weights_map.size());
    for (const auto & it : ml.weights_map) {
        const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id));
        if (remapped_name.empty()) {
            LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
            continue;
        }

        if (remapped_name != it.first) {
            ggml_set_name(it.second.tensor, remapped_name.c_str());
            LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor));
        }
        tensors.push_back(&it.second);
    }
    if (!prune_list.empty()) {
        gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_BLOCK_COUNT).c_str(), blk_id);
    }

    // keep_split requires that the weights are sorted by split index
    if (params->keep_split) {
        std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) {
            if (a->idx == b->idx) {
                return a->offs < b->offs;
            }
            return a->idx < b->idx;
        });
    }

    // compute tensor metadata once and cache it
    std::vector<tensor_metadata> metadata(tensors.size());
    for (size_t i = 0; i < tensors.size(); ++i) {
        metadata[i].name = ggml_get_name(tensors[i]->tensor);
    }

    // initialize quantization state counters and metadata categories
    init_quantize_state_counters(qs, metadata);

    int idx = 0;
    uint16_t n_split = 1;

    // Assume split index is continuous
    if (params->keep_split) {
        for (const auto * it : tensors) {
            n_split = std::max(uint16_t(it->idx + 1), n_split);
        }
    }
    std::vector<gguf_context_ptr> ctx_outs(n_split);
    ctx_outs[0] = std::move(ctx_out);

    // flag for --dry-run
    bool will_require_imatrix = false;

    //
    // preliminary iteration over all weights
    //

    for (size_t i = 0; i < tensors.size(); ++i) {
        const auto * it = tensors[i];
        const struct ggml_tensor * tensor = it->tensor;

        uint16_t i_split = params->keep_split ? it->idx : 0;
        if (!ctx_outs[i_split]) {
            ctx_outs[i_split].reset(gguf_init_empty());
        }
        gguf_add_tensor(ctx_outs[i_split].get(), tensor);

        metadata[i].allows_quantization = tensor_allows_quantization(params, model->arch, tensor);

        if (metadata[i].allows_quantization) {
            metadata[i].target_type = llama_tensor_get_type(qs, params, tensor, default_type, metadata[i]);
        } else {
            metadata[i].target_type = tensor->type;
        }

        metadata[i].requires_imatrix = tensor_requires_imatrix(tensor->name, metadata[i].target_type, ftype);

        if (params->imatrix) {
            metadata[i].remapped_imatrix_name = remap_imatrix(tensor->name, mapped);
        } else if (metadata[i].allows_quantization && metadata[i].requires_imatrix) {
            if (params->dry_run) {
                will_require_imatrix = true;
            } else {
                LLAMA_LOG_ERROR("\n============================================================================\n"
                                " ERROR: this quantization requires an importance matrix!\n"
                                "        - offending tensor: %s\n"
                                "        - target type: %s\n"
                                "============================================================================\n\n",
                                metadata[i].name.c_str(), ggml_type_name(metadata[i].target_type));
                throw std::runtime_error("this quantization requires an imatrix!");
            }
        }
    }

    // Set split info if needed
    if (n_split > 1) {
        for (size_t i = 0; i < ctx_outs.size(); ++i) {
            gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
            gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
            gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), (int32_t)tensors.size());
        }
    }

    size_t total_size_org = 0;
    size_t total_size_new = 0;

    std::vector<std::thread> workers;
    workers.reserve(nthread);

    std::vector<no_init<uint8_t>> read_data;
    std::vector<no_init<uint8_t>> work;
    std::vector<no_init<float>> f32_conv_buf;

    const size_t max_buf_size = params->max_buf_size ? params->max_buf_size : LLAMA_QUANT_MAX_BUF_SIZE;

    int cur_split = -1;
    std::ofstream fout;
    auto close_ofstream = [&]() {
        // Write metadata and close file handler
        if (fout.is_open()) {
            fout.seekp(0);
            std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split].get()));
            gguf_get_meta_data(ctx_outs[cur_split].get(), data.data());
            fout.write((const char *) data.data(), data.size());
            fout.close();
        }
    };
    auto new_ofstream = [&](int index) {
        cur_split = index;
        GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
        std::string fname = fname_out;
        if (params->keep_split) {
            std::vector<char> split_path(llama_path_max(), 0);
            llama_split_path(split_path.data(), split_path.size(), fname_out.c_str(), cur_split, n_split);
            fname = std::string(split_path.data());
        }

        fout = std::ofstream(fname, std::ios::binary);
        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
        const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split].get());
        // placeholder for the meta data
        ::zeros(fout, meta_size);
    };

    // no output file for --dry-run
    if (!params->dry_run) {
        new_ofstream(0);
    }

    //
    // main loop: iterate over all weights
    //

    for (size_t i = 0; i < tensors.size(); ++i) {
        const auto & weight = *tensors[i];
        const auto & tm = metadata[i];
        ggml_tensor * tensor = weight.tensor;

        if (!params->dry_run && (weight.idx != cur_split && params->keep_split)) {
            close_ofstream();
            new_ofstream(weight.idx);
        }

        const size_t tensor_size = ggml_nbytes(tensor);

        // read a byte range of the current tensor
        auto load_range = [&](size_t offs, size_t size) -> const void * {
            if (!ml.use_mmap && read_data.size() < size) {
                read_data.resize(size);
            }
            return ml.load_data_range(weight, offs, size, read_data.data());
        };

        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));

        const ggml_type cur_type = tensor->type;
        const ggml_type new_type = tm.target_type;

        // If we've decided to quantize to the same type the tensor is already
        // in then there's nothing to do.
        bool quantize = cur_type != new_type;

        size_t new_size;

        if (params->dry_run) {
            // the --dry-run option calculates the final quantization size without quantizing
            if (quantize) {
                new_size = ggml_nrows(tensor) * ggml_row_size(new_type, tensor->ne[0]);
                LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB (%s)\n",
                               tensor_size/1024.0/1024.0,
                               new_size/1024.0/1024.0,
                               ggml_type_name(new_type));
                if (!will_require_imatrix && tm.requires_imatrix) {
                    will_require_imatrix = true;
                }
            } else {
                new_size = tensor_size;
                LLAMA_LOG_INFO("size = %8.3f MiB\n", new_size/1024.0/1024.0);
            }
            total_size_org += tensor_size;
            total_size_new += new_size;
            continue;
        } else {
            // no --dry-run, perform quantization
            if (!quantize) {
                new_size = tensor_size;
                LLAMA_LOG_INFO("size = %8.3f MiB\n", tensor_size/1024.0/1024.0);

                // copy in slabs of whole rows, so that each slab can be validated
                const size_t row_size   = ggml_row_size(tensor->type, tensor->ne[0]);
                const size_t slab_size  = std::max<size_t>(row_size, (max_buf_size/row_size)*row_size);

                for (size_t offs = 0; offs < tensor_size; offs += slab_size) {
                    const size_t size = std::min(slab_size, tensor_size - offs);
                    fout.write((const char *) load_range(offs, size), size);
                }
            } else {
                const float * imatrix = nullptr;
                if (imatrix_data) {
                    auto it = imatrix_data->find(tm.remapped_imatrix_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]*tensor->ne[2]) {
                            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->ne[2]), tensor->name);

                            // this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
                            // this is a significant error and it may be good idea to abort the process if this happens,
                            // since many people will miss the error and not realize that most of the model is being quantized without an imatrix
                            // tok_embd should be ignored in this case, since it always causes this warning
                            if (!tensor_name_match_token_embd(tensor->name)) {
                                throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
                                        int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
                            }
                        }
                    }
                }
                if (!imatrix && tm.requires_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));
                }

                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)));
                }

                LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
                fflush(stdout);

                const int64_t n_per_row = tensor->ne[0];
                const int64_t nrows_per_expert = tensor->ne[1];
                const int64_t nrows_total = tensor->ne[1] * tensor->ne[2];

                const size_t row_size_src = ggml_row_size(tensor->type, n_per_row);
                const size_t row_size_dst = ggml_row_size(new_type,     n_per_row);

                // process the rows in slabs, so that the buffers stay below max_buf_size
                const size_t bytes_per_row = row_size_src + row_size_dst + (tensor->type == GGML_TYPE_F32 ? 0 : n_per_row*sizeof(float));
                const int64_t nrows_slab = std::max<int64_t>(1, std::min<int64_t>(nrows_total, max_buf_size/bytes_per_row));

                static const int64_t min_chunk_size = 32 * 512;
                const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row));

                // process rows across all experts in one pass to keep all threads busy
                new_size = 0;
                for (int64_t ir = 0; ir < nrows_total; ir += nrows_slab) {
                    const int64_t nrows_cur = std::min(nrows_slab, nrows_total - ir);
                    const int64_t nelements_cur = nrows_cur * n_per_row;

                    const void * src = load_range(ir*row_size_src, nrows_cur*row_size_src);

                    const float * f32_data;
                    if (tensor->type == GGML_TYPE_F32) {
                        f32_data = (const float *) src;
                    } else {
                        if (f32_conv_buf.size() < (size_t) nelements_cur) {
                            f32_conv_buf.resize(nelements_cur);
                        }
                        llama_tensor_dequantize_impl(tensor->type, src, (float *) f32_conv_buf.data(), workers, nelements_cur, nthread);
                        f32_data = (const float *) f32_conv_buf.data();
                    }

                    if (work.size() < nrows_cur*row_size_dst) {
                        work.resize(nrows_cur*row_size_dst);
                    }

                    const int64_t nchunk = (nelements_cur + chunk_size - 1)/chunk_size;
                    const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;

                    const size_t size_cur = llama_tensor_quantize_impl(new_type, f32_data, work.data(), chunk_size, ir, nrows_cur, nrows_per_expert, n_per_row, imatrix, workers, nthread_use);

                    fout.write((const char *) work.data(), size_cur);
                    new_size += size_cur;
                }
                LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", tensor_size/1024.0/1024.0, new_size/1024.0/1024.0);
            }
            total_size_org += tensor_size;
            total_size_new += new_size;

            // update the gguf metadata as we go
            gguf_set_tensor_type(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_type);
            GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), metadata[i].name.c_str())) == new_size);

            // tensor data is already written, add the padding
            zeros(fout, GGML_PAD(new_size, align) - new_size);

            // unmap the tensor to free memory
            if (ml.use_mmap) { ml.unmap_weight(weight); }

        } // no --dry-run
    } // main loop

    if (!params->dry_run) {
        close_ofstream();
    }

    LLAMA_LOG_INFO("%s: model size  = %8.2f MiB (%.2f BPW)\n", __func__, total_size_org/1024.0/1024.0, total_size_org*8.0/ml.n_elements);
    LLAMA_LOG_INFO("%s: quant size  = %8.2f MiB (%.2f BPW)\n", __func__, total_size_new/1024.0/1024.0, total_size_new*8.0/ml.n_elements);

    if (!params->imatrix && params->dry_run && will_require_imatrix) {
        LLAMA_LOG_WARN("%s: WARNING: dry run completed successfully, but actually completing this quantization will require an imatrix!\n",
                       __func__
        );
    }

    if (qs.n_fallback > 0) {
        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
                __func__, qs.n_fallback, ml.n_tensors);
    }
}

//
// interface implementation
//

llama_model_quantize_params llama_model_quantize_default_params() {
    llama_model_quantize_params result = {
        /*.nthread                     =*/ 0,
        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q8_0,
        /*.output_tensor_type          =*/ GGML_TYPE_COUNT,
        /*.token_embedding_type        =*/ GGML_TYPE_COUNT,
        /*.allow_requantize            =*/ false,
        /*.quantize_output_tensor      =*/ true,
        /*.only_copy                   =*/ false,
        /*.pure                        =*/ false,
        /*.keep_split                  =*/ false,
        /*.dry_run                     =*/ false,
        /*.imatrix                     =*/ nullptr,
        /*.kv_overrides                =*/ nullptr,
        /*.tensor_type                 =*/ nullptr,
        /*.prune_layers                =*/ nullptr,
        /*.max_buf_size                =*/ LLAMA_QUANT_MAX_BUF_SIZE
    };

    return result;
}

uint32_t llama_model_quantize(
        const char * fname_inp,
        const char * fname_out,
        const llama_model_quantize_params * params) {
    try {
        llama_model_quantize_impl(fname_inp, fname_out, params);
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
        return 1;
    }

    return 0;
}

//
// Helper functions for external tools exposed in llama-ext.h
//

quantize_state_impl * llama_quant_init(
        const llama_model * model,
        const llama_model_quantize_params * params) {
    return new quantize_state_impl(*model, params);
}

void llama_quant_free(quantize_state_impl * qs) {
    delete qs;
}

llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * desc) {
    struct llama_model_params mparams = llama_model_default_params();
    auto arch = llm_arch_from_string(desc->architecture);
    auto * model = llama_model_create(arch, mparams);
    model->arch = arch;

    // infer llm_type: only LLM_TYPE_70B matters for quantization logic
    if (model->arch == LLM_ARCH_LLAMA && desc->n_layer == 80 && desc->n_head != desc->n_head_kv) {
        model->type = LLM_TYPE_70B;
    }

    model->hparams.n_embd             = desc->n_embd;
    model->hparams.n_embd_head_k_full = desc->n_embd_head_k;
    model->hparams.n_embd_head_v_full = desc->n_embd_head_v;
    model->hparams.n_layer_all        = desc->n_layer;
    GGML_ASSERT(desc->n_layer > 0 && desc->n_layer <= LLAMA_MAX_LAYERS);
    model->hparams.n_expert           = desc->n_expert;

    for (uint32_t i = 0; i < desc->n_layer; i++) {
        model->hparams.n_head_arr[i]    = desc->n_head;
        model->hparams.n_head_kv_arr[i] = desc->n_head_kv;
        model->hparams.n_ff_arr[i]      = desc->n_ff;
    }

    return model;
}

bool llama_quant_tensor_allows_quantization(
        const quantize_state_impl * qs,
        const ggml_tensor * tensor) {
    return tensor_allows_quantization(qs->params, qs->model.arch, tensor);
}

void llama_quant_compute_types(
        quantize_state_impl * qs,
        llama_ftype ftype,
        ggml_tensor ** tensors,
        ggml_type * result_types,
        size_t n_tensors) {
    // reset per-computation state
    qs->n_attention_wv      = 0;
    qs->n_ffn_down          = 0;
    qs->n_ffn_gate          = 0;
    qs->n_ffn_up            = 0;
    qs->i_attention_wv      = 0;
    qs->i_ffn_down          = 0;
    qs->i_ffn_gate          = 0;
    qs->i_ffn_up            = 0;
    qs->n_fallback          = 0;
    qs->has_imatrix         = false;
    qs->has_tied_embeddings = true;

    // build metadata from tensor names
    std::vector<tensor_metadata> metadata(n_tensors);
    for (size_t i = 0; i < n_tensors; i++) {
        metadata[i].name = ggml_get_name(tensors[i]);
    }

    // initialize counters and categories
    init_quantize_state_counters(*qs, metadata);

    // use a local copy of params with the requested ftype
    llama_model_quantize_params local_params = *qs->params;
    local_params.ftype = ftype;

    ggml_type default_type = llama_ftype_get_default_type(ftype);

    // compute types
    for (size_t i = 0; i < n_tensors; i++) {
        result_types[i] = llama_tensor_get_type(*qs, &local_params, tensors[i], default_type, metadata[i]);
    }
}