EADST

llama.cpp: Definations of Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, and Q8_K Structures

The source code from llama.cpp /ggml-quants.c includes detailed definitions of various quantization structures used in neural networks and computational models. These structures, named Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, and Q8_K, are designed for efficient representation and processing of weights in a quantized format, reducing memory footprint while maintaining acceptable levels of accuracy.

//
// Super-block quantization structures
//

// Define the super-block size based on a preprocessor directive. 
// This affects the size of quantization blocks and related arrays.
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif

// 2-bit quantization structure
// Each weight is represented as x = a * q + b, where a is the scale and b is the minimum value.
// The structure is divided into 16 blocks of 16 elements each, leading to 2.625 bits per weight.

// When QK_K = 256, then scales = 16 bytes, qs = 64 bytes, d = 2 bytes, dmin = 2 bytes. The total is 84 bytes = 84 * 8  bits = 672 bits, so have 672 bits / 256 = 2.625 (bpw) bits per weight.

typedef struct {
    uint8_t scales[QK_K/16];    // Scales and minimums, quantized using 4 bits.
    uint8_t qs[QK_K/4];         // Quantized values.
    ggml_fp16_t d;              // Super-block scale for quantized scales.
    ggml_fp16_t dmin;           // Super-block scale for quantized minimums.
} block_q2_K;
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");

// 3-bit quantization structure
// Weights are represented as x = a * q, using only the scale factor a.
// Divided into 16 blocks of 16 elements each, this achieves 3.4375 bits per weight.
#ifdef GGML_QKK_64
typedef struct {
    uint8_t hmask[QK_K/8];    // High bit of the quantized values.
    uint8_t qs[QK_K/4];       // Low 2 bits of the quantized values.
    uint8_t scales[2];        // Scale values.
    ggml_fp16_t d;            // Super-block scale.
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
#else

// When QK_K = 256, then hmask= 32 bytes, qs = 64 bytes, scales = 12 bytes, d = 2 bytes. The total is 110 bytes = 110 * 8  bits = 880 bits, so we have 880 bits / 256 = 3.4375 (bpw) bits per weight.

typedef struct {
    uint8_t hmask[QK_K/8];    // High bit of the quantized values.
    uint8_t qs[QK_K/4];       // Low 2 bits of the quantized values.
    uint8_t scales[12];       // Scales, quantized with 6 bits.
    ggml_fp16_t d;            // Super-block scale.
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
#endif

// 4-bit quantization structure
// Weights are again represented as x = a * q + b.
// The structure is divided into 8 blocks of 32 elements each, achieving 4.5 bits per weight.
#ifdef GGML_QKK_64
typedef struct {
    ggml_fp16_t d[2];         // Super-block scales/mins.
    uint8_t scales[2];        // 4-bit block scales/mins.
    uint8_t qs[QK_K/2];       // 4-bit quantized values.
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
    ggml_fp16_t d;            // Super-block scale for quantized scales.
    ggml_fp16_t dmin;         // Super-block scale for quantized mins.
    uint8_t scales[K_SCALE_SIZE]; // Scales and mins, quantized with 6 bits.
    uint8_t qs[QK_K/2];       // 4-bit quantized values.
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
#endif

// 5-bit quantization structure
// Weights are represented as x = a * q + b.
// The structure is divided into 8 blocks of 32 elements each, achieving 5.5 bits per weight.
#ifdef GGML_QKK_64
typedef struct {
    ggml_fp16_t d;            // Super-block scale.
    int8_t  scales[QK_K/16];  // 8-bit block scales.
    uint8_t qh[QK_K/8];       // High bit of the quantized values.
    uint8_t qs[QK_K/2];       // Low 4 bits of the quantized values.
} block_q5_K;
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
    ggml_fp16_t d;            // Super-block scale for quantized scales.
    ggml_fp16_t dmin;         // Super-block scale for quantized mins.
    uint8_t scales[K_SCALE_SIZE]; // Scales and mins, quantized with 6 bits.
    uint8_t qh[QK_K/8];       // High bit of the quantized values.
    uint8_t qs[QK_K/2];       // Low 4 bits of the quantized values.
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif

// 6-bit quantization structure
// Weights are represented as x = a * q.
// The structure is divided into 16 blocks of 16 elements each, achieving 6.5625 bits per weight.
typedef struct {
    uint8_t ql[QK_K/2];       // Lower 4 bits of the quantized values.
    uint8_t qh[QK_K/4];       // Upper 2 bits of the quantized values.
    int8_t  scales[QK_K/16];  // Scales, quantized with 8 bits.
    ggml_fp16_t d;            // Super-block scale.
} block_q6_K;
static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");

// Intermediate quantization and dot product structure
typedef struct {
    float   d;               // Delta value for quantization.
    int8_t  qs[QK_K];        // Quantized values.
    int16_t bsums[QK_K/16];  // Sum of quants in groups of 16.
} block_q8_K;
static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");

// "True" 2-bit quantization structure, adjusted for block usage in ggml design.
// Results in 2.0625 bits per weight due to 16-bit scale for each block of 256.
typedef struct {
    ggml_fp16_t d;           // Super-block scale.
    uint16_t qs[QK_K/8];     // Quantized values.
} block_iq2_xxs;
static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");

// 2.3125 bpw (bits per weight) quantization structure
typedef struct {
    ggml_fp16_t d;           // Super-block scale.
    uint16_t qs[QK_K/8];     // Quantized values.
    uint8_t  scales[QK_K/32];// Scales for quantization.
} block_iq2_xs;
static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); 
相关标签
About Me
XD
Goals determine what you are going to be.
Category
标签云
Bin Math Vim PyCharm NLTK CTC Land Breakpoint PDB PDF CLAP SQLite Bert CUDA Numpy CEIR Bipartite XGBoost Quantization Freesound Color v2ray Website Algorithm 阿里云 Windows FP32 Baidu Search Django FP8 搞笑 签证 JSON DeepSeek C++ torchinfo 多进程 Paper 域名 Rebuttal Pillow Plotly Image2Text ResNet-50 Crawler 第一性原理 GoogLeNet 报税 Template tar Video Tracking transformers Michelin Qwen2.5 WAN PyTorch GPT4 Plate 强化学习 UI Qwen RAR CC Heatmap Tensor SPIE Streamlit DeepStream Transformers Github Jupyter 顶会 FP16 TTS PIP TSV Hungarian Translation OCR 音频 CV scipy HuggingFace 公式 Animate Python XML Docker Pytorch ModelScope 多线程 mmap 财报 WebCrawler printf TensorRT 图形思考法 Tiktoken LaTeX Web LeetCode tqdm Linux Augmentation uWSGI FP64 Conda Dataset Vmess SAM News Domain 版权 Sklearn diffusers VPN SVR 关于博主 QWEN Pandas AI Diagram Gemma VGG-16 Agent Ptyhon Password Bitcoin Pickle NLP RGB LLM Anaconda Knowledge Attention Mixtral YOLO Jetson Random git-lfs Interview COCO llama.cpp Claude Shortcut Firewall OpenCV Nginx Miniforge Safetensors Base64 ChatGPT Ubuntu NameSilo hf LoRA Quantize BTC Data 净利润 VSCode Input Excel Paddle InvalidArgumentError Hilton Qwen2 ONNX EXCEL Clash GIT Cloudreve Proxy Review Google SQL 图标 logger Git 云服务器 Statistics API Llama FlashAttention BF16 Logo 飞书 TensorFlow MD5 腾讯云 证件照 Use GGML CAM LLAMA uwsgi 递归学习法 Markdown HaggingFace UNIX OpenAI Datetime Hotel IndexTTS2 Permission CSV 算法题 Distillation v0.dev GPTQ Card git icon BeautifulSoup Disk Magnet Food 继承 Zip FastAPI
站点统计

本站现有博文323篇,共被浏览795547

本站已经建立2493天!

热门文章
文章归档
回到顶部