llama.cpp/gguf-py/gguf/constants.py

from __future__ import annotations

import sys
from enum import Enum, IntEnum, auto
from typing import Any

#
# constants
#

GGUF_MAGIC             = 0x46554747  # "GGUF"
GGUF_VERSION           = 3
GGUF_DEFAULT_ALIGNMENT = 32

#
# metadata keys
#


class Keys:
    class General:
        ARCHITECTURE         = "general.architecture"
        QUANTIZATION_VERSION = "general.quantization_version"
        ALIGNMENT            = "general.alignment"
        NAME                 = "general.name"
        AUTHOR               = "general.author"
        URL                  = "general.url"
        DESCRIPTION          = "general.description"
        LICENSE              = "general.license"
        SOURCE_URL           = "general.source.url"
        SOURCE_HF_REPO       = "general.source.huggingface.repository"
        FILE_TYPE            = "general.file_type"

    class LLM:
        VOCAB_SIZE            = "{arch}.vocab_size"
        CONTEXT_LENGTH        = "{arch}.context_length"
        EMBEDDING_LENGTH      = "{arch}.embedding_length"
        BLOCK_COUNT           = "{arch}.block_count"
        FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
        USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
        TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
        EXPERT_COUNT          = "{arch}.expert_count"
        EXPERT_USED_COUNT     = "{arch}.expert_used_count"
        POOLING_TYPE          = "{arch}.pooling_type"

    class Attention:
        HEAD_COUNT        = "{arch}.attention.head_count"
        HEAD_COUNT_KV     = "{arch}.attention.head_count_kv"
        MAX_ALIBI_BIAS    = "{arch}.attention.max_alibi_bias"
        CLAMP_KQV         = "{arch}.attention.clamp_kqv"
        KEY_LENGTH        = "{arch}.attention.key_length"
        VALUE_LENGTH      = "{arch}.attention.value_length"
        LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
        LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
        CAUSAL            = "{arch}.attention.causal"

    class Rope:
        DIMENSION_COUNT      = "{arch}.rope.dimension_count"
        FREQ_BASE            = "{arch}.rope.freq_base"
        SCALING_TYPE         = "{arch}.rope.scaling.type"
        SCALING_FACTOR       = "{arch}.rope.scaling.factor"
        SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
        SCALING_FINETUNED    = "{arch}.rope.scaling.finetuned"

    class SSM:
        CONV_KERNEL    = "{arch}.ssm.conv_kernel"
        INNER_SIZE     = "{arch}.ssm.inner_size"
        STATE_SIZE     = "{arch}.ssm.state_size"
        TIME_STEP_RANK = "{arch}.ssm.time_step_rank"

    class Tokenizer:
        MODEL            = "tokenizer.ggml.model"
        LIST             = "tokenizer.ggml.tokens"
        TOKEN_TYPE       = "tokenizer.ggml.token_type"
        TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count"  # for BERT-style token types
        SCORES           = "tokenizer.ggml.scores"
        MERGES           = "tokenizer.ggml.merges"
        BOS_ID           = "tokenizer.ggml.bos_token_id"
        EOS_ID           = "tokenizer.ggml.eos_token_id"
        UNK_ID           = "tokenizer.ggml.unknown_token_id"
        SEP_ID           = "tokenizer.ggml.seperator_token_id"
        PAD_ID           = "tokenizer.ggml.padding_token_id"
        CLS_ID           = "tokenizer.ggml.cls_token_id"
        MASK_ID          = "tokenizer.ggml.mask_token_id"
        ADD_BOS          = "tokenizer.ggml.add_bos_token"
        ADD_EOS          = "tokenizer.ggml.add_eos_token"
        ADD_PREFIX       = "tokenizer.ggml.add_space_prefix"
        HF_JSON          = "tokenizer.huggingface.json"
        RWKV             = "tokenizer.rwkv.world"
        CHAT_TEMPLATE    = "tokenizer.chat_template"


#
# recommended mapping of model tensor names for storage in gguf
#


class MODEL_ARCH(IntEnum):
    LLAMA      = auto()
    FALCON     = auto()
    BAICHUAN   = auto()
    GPT2       = auto()
    GPTJ       = auto()
    GPTNEOX    = auto()
    MPT        = auto()
    STARCODER  = auto()
    PERSIMMON  = auto()
    REFACT     = auto()
    BERT       = auto()
    NOMIC_BERT = auto()
    BLOOM      = auto()
    STABLELM   = auto()
    QWEN       = auto()
    QWEN2      = auto()
    PHI2       = auto()
    PLAMO      = auto()
    CODESHELL  = auto()
    ORION      = auto()
    INTERNLM2  = auto()
    MINICPM    = auto()
    GEMMA      = auto()
    STARCODER2 = auto()
    MAMBA      = auto()


class MODEL_TENSOR(IntEnum):
    TOKEN_EMBD      = auto()
    TOKEN_EMBD_NORM = auto()
    TOKEN_TYPES     = auto()
    POS_EMBD        = auto()
    OUTPUT          = auto()
    OUTPUT_NORM     = auto()
    ROPE_FREQS      = auto()
    ATTN_Q          = auto()
    ATTN_K          = auto()
    ATTN_V          = auto()
    ATTN_QKV        = auto()
    ATTN_OUT        = auto()
    ATTN_NORM       = auto()
    ATTN_NORM_2     = auto()
    ATTN_OUT_NORM   = auto()
    ATTN_ROT_EMBD   = auto()
    FFN_GATE_INP    = auto()
    FFN_NORM        = auto()
    FFN_GATE        = auto()
    FFN_DOWN        = auto()
    FFN_UP          = auto()
    FFN_ACT         = auto()
    FFN_GATE_EXP    = auto()
    FFN_DOWN_EXP    = auto()
    FFN_UP_EXP      = auto()
    ATTN_Q_NORM     = auto()
    ATTN_K_NORM     = auto()
    LAYER_OUT_NORM  = auto()
    SSM_IN          = auto()
    SSM_CONV1D      = auto()
    SSM_X           = auto()
    SSM_DT          = auto()
    SSM_A           = auto()
    SSM_D           = auto()
    SSM_OUT         = auto()


MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.LLAMA:          "llama",
    MODEL_ARCH.FALCON:         "falcon",
    MODEL_ARCH.BAICHUAN:       "baichuan",
    MODEL_ARCH.GPT2:           "gpt2",
    MODEL_ARCH.GPTJ:           "gptj",
    MODEL_ARCH.GPTNEOX:        "gptneox",
    MODEL_ARCH.MPT:            "mpt",
    MODEL_ARCH.STARCODER:      "starcoder",
    MODEL_ARCH.PERSIMMON:      "persimmon",
    MODEL_ARCH.REFACT:         "refact",
    MODEL_ARCH.BERT:           "bert",
    MODEL_ARCH.NOMIC_BERT:     "nomic-bert",
    MODEL_ARCH.BLOOM:          "bloom",
    MODEL_ARCH.STABLELM:       "stablelm",
    MODEL_ARCH.QWEN:           "qwen",
    MODEL_ARCH.QWEN2:          "qwen2",
    MODEL_ARCH.PHI2:           "phi2",
    MODEL_ARCH.PLAMO:          "plamo",
    MODEL_ARCH.CODESHELL:      "codeshell",
    MODEL_ARCH.ORION:          "orion",
    MODEL_ARCH.INTERNLM2:      "internlm2",
    MODEL_ARCH.MINICPM:        "minicpm",
    MODEL_ARCH.GEMMA:          "gemma",
    MODEL_ARCH.STARCODER2:     "starcoder2",
    MODEL_ARCH.MAMBA:          "mamba",
}

TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.TOKEN_EMBD:      "token_embd",
    MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
    MODEL_TENSOR.TOKEN_TYPES:     "token_types",
    MODEL_TENSOR.POS_EMBD:        "position_embd",
    MODEL_TENSOR.OUTPUT_NORM:     "output_norm",
    MODEL_TENSOR.OUTPUT:          "output",
    MODEL_TENSOR.ROPE_FREQS:      "rope_freqs",
    MODEL_TENSOR.ATTN_NORM:       "blk.{bid}.attn_norm",
    MODEL_TENSOR.ATTN_NORM_2:     "blk.{bid}.attn_norm_2",
    MODEL_TENSOR.ATTN_QKV:        "blk.{bid}.attn_qkv",
    MODEL_TENSOR.ATTN_Q:          "blk.{bid}.attn_q",
    MODEL_TENSOR.ATTN_K:          "blk.{bid}.attn_k",
    MODEL_TENSOR.ATTN_V:          "blk.{bid}.attn_v",
    MODEL_TENSOR.ATTN_OUT:        "blk.{bid}.attn_output",
    MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
    MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
    MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
    MODEL_TENSOR.ATTN_OUT_NORM:   "blk.{bid}.attn_output_norm",
    MODEL_TENSOR.FFN_GATE_INP:    "blk.{bid}.ffn_gate_inp",
    MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
    MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
    MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
    MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
    MODEL_TENSOR.FFN_ACT:         "blk.{bid}.ffn",
    MODEL_TENSOR.FFN_GATE_EXP:    "blk.{bid}.ffn_gate.{xid}",
    MODEL_TENSOR.FFN_DOWN_EXP:    "blk.{bid}.ffn_down.{xid}",
    MODEL_TENSOR.FFN_UP_EXP:      "blk.{bid}.ffn_up.{xid}",
    MODEL_TENSOR.LAYER_OUT_NORM:  "blk.{bid}.layer_output_norm",
    MODEL_TENSOR.SSM_IN:          "blk.{bid}.ssm_in",
    MODEL_TENSOR.SSM_CONV1D:      "blk.{bid}.ssm_conv1d",
    MODEL_TENSOR.SSM_X:           "blk.{bid}.ssm_x",
    MODEL_TENSOR.SSM_DT:          "blk.{bid}.ssm_dt",
    MODEL_TENSOR.SSM_A:           "blk.{bid}.ssm_a",
    MODEL_TENSOR.SSM_D:           "blk.{bid}.ssm_d",
    MODEL_TENSOR.SSM_OUT:         "blk.{bid}.ssm_out",
}

MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
    MODEL_ARCH.LLAMA: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_GATE_INP,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.FFN_GATE_EXP,
        MODEL_TENSOR.FFN_DOWN_EXP,
        MODEL_TENSOR.FFN_UP_EXP,
    ],
    MODEL_ARCH.GPTNEOX: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.FALCON: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_NORM_2,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.BAICHUAN: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.STARCODER: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.POS_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.BERT: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.TOKEN_EMBD_NORM,
        MODEL_TENSOR.TOKEN_TYPES,
        MODEL_TENSOR.POS_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.ATTN_OUT_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.LAYER_OUT_NORM,
    ],
    MODEL_ARCH.NOMIC_BERT: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.TOKEN_EMBD_NORM,
        MODEL_TENSOR.TOKEN_TYPES,
        MODEL_TENSOR.POS_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.ATTN_OUT_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.LAYER_OUT_NORM,
    ],
    MODEL_ARCH.MPT: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.FFN_ACT,
    ],
    MODEL_ARCH.GPTJ: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.PERSIMMON: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.ATTN_Q_NORM,
        MODEL_TENSOR.ATTN_K_NORM,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
    MODEL_ARCH.REFACT: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.BLOOM: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.TOKEN_EMBD_NORM,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.STABLELM: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.QWEN: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.QWEN2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.PLAMO: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.GPT2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.POS_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.PHI2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.CODESHELL: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.POS_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.ORION: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.INTERNLM2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.MINICPM: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_GATE_INP,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.FFN_GATE_EXP,
        MODEL_TENSOR.FFN_DOWN_EXP,
        MODEL_TENSOR.FFN_UP_EXP,
    ],
    MODEL_ARCH.GEMMA: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_GATE,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.FFN_NORM,
    ],
    MODEL_ARCH.STARCODER2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.ATTN_ROT_EMBD,
        MODEL_TENSOR.FFN_NORM,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
    MODEL_ARCH.MAMBA: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.SSM_IN,
        MODEL_TENSOR.SSM_CONV1D,
        MODEL_TENSOR.SSM_X,
        MODEL_TENSOR.SSM_DT,
        MODEL_TENSOR.SSM_A,
        MODEL_TENSOR.SSM_D,
        MODEL_TENSOR.SSM_OUT,
    ],
    # TODO
}

# tensors that will not be serialized
MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
    MODEL_ARCH.LLAMA: [
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
    MODEL_ARCH.BAICHUAN: [
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
    MODEL_ARCH.PERSIMMON: [
        MODEL_TENSOR.ROPE_FREQS,
    ],
    MODEL_ARCH.QWEN: [
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
    MODEL_ARCH.CODESHELL: [
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
    MODEL_ARCH.ORION: [
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
    MODEL_ARCH.STARCODER2: [
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
}

#
# types
#


class TokenType(IntEnum):
    NORMAL       = 1
    UNKNOWN      = 2
    CONTROL      = 3
    USER_DEFINED = 4
    UNUSED       = 5
    BYTE         = 6


class RopeScalingType(Enum):
    NONE   = 'none'
    LINEAR = 'linear'
    YARN   = 'yarn'


class PoolingType(IntEnum):
    NONE = 0
    MEAN = 1
    CLS  = 2


class GGMLQuantizationType(IntEnum):
    F32     = 0
    F16     = 1
    Q4_0    = 2
    Q4_1    = 3
    Q5_0    = 6
    Q5_1    = 7
    Q8_0    = 8
    Q8_1    = 9
    Q2_K    = 10
    Q3_K    = 11
    Q4_K    = 12
    Q5_K    = 13
    Q6_K    = 14
    Q8_K    = 15
    IQ2_XXS = 16
    IQ2_XS  = 17
    IQ3_XXS = 18
    IQ1_S   = 19
    IQ4_NL  = 20
    IQ3_S   = 21
    IQ2_S   = 22
    IQ4_XS  = 23
    I8      = 24
    I16     = 25
    I32     = 26
    I64     = 27
    F64     = 28


class GGUFEndian(IntEnum):
    LITTLE = 0
    BIG = 1


class GGUFValueType(IntEnum):
    UINT8   = 0
    INT8    = 1
    UINT16  = 2
    INT16   = 3
    UINT32  = 4
    INT32   = 5
    FLOAT32 = 6
    BOOL    = 7
    STRING  = 8
    ARRAY   = 9
    UINT64  = 10
    INT64   = 11
    FLOAT64 = 12

    @staticmethod
    def get_type(val: Any) -> GGUFValueType:
        if isinstance(val, (str, bytes, bytearray)):
            return GGUFValueType.STRING
        elif isinstance(val, list):
            return GGUFValueType.ARRAY
        elif isinstance(val, float):
            return GGUFValueType.FLOAT32
        elif isinstance(val, bool):
            return GGUFValueType.BOOL
        elif isinstance(val, int):
            return GGUFValueType.INT32
        # TODO: need help with 64-bit types in Python
        else:
            print("Unknown type:", type(val))
            sys.exit()


# Note: Does not support GGML_QKK_64
QK_K = 256
# Items here are (block size, type size)
GGML_QUANT_SIZES = {
    GGMLQuantizationType.F32:     (1, 4),
    GGMLQuantizationType.F16:     (1, 2),
    GGMLQuantizationType.Q4_0:    (32, 2 + 16),
    GGMLQuantizationType.Q4_1:    (32, 2 + 2 + 16),
    GGMLQuantizationType.Q5_0:    (32, 2 + 4 + 16),
    GGMLQuantizationType.Q5_1:    (32, 2 + 2 + 4 + 16),
    GGMLQuantizationType.Q8_0:    (32, 2 + 32),
    GGMLQuantizationType.Q8_1:    (32, 4 + 4 + 32),
    GGMLQuantizationType.Q2_K:    (256, 2 + 2 + QK_K // 16 + QK_K // 4),
    GGMLQuantizationType.Q3_K:    (256, 2 + QK_K // 4 + QK_K // 8 + 12),
    GGMLQuantizationType.Q4_K:    (256, 2 + 2 + QK_K // 2 + 12),
    GGMLQuantizationType.Q5_K:    (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
    GGMLQuantizationType.Q6_K:    (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
    GGMLQuantizationType.Q8_K:    (256, 4 + QK_K + QK_K // 8),
    GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4),
    GGMLQuantizationType.IQ2_XS:  (256, 2 + QK_K // 4 + QK_K // 32),
    GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8),
    GGMLQuantizationType.IQ1_S:   (256, 2 + QK_K // 8 + QK_K // 16),
    GGMLQuantizationType.IQ4_NL:  (32, 2 + 16),
    GGMLQuantizationType.IQ3_S:   (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
    GGMLQuantizationType.IQ2_S:   (256, 2 + QK_K // 4 + QK_K // 16),
    GGMLQuantizationType.IQ4_XS:  (256, 2 + 2 + QK_K // 2 + QK_K // 64),
    GGMLQuantizationType.I8:      (1, 1),
    GGMLQuantizationType.I16:     (1, 2),
    GGMLQuantizationType.I32:     (1, 4),
    GGMLQuantizationType.I64:     (1, 8),
    GGMLQuantizationType.F64:     (1, 8),
}


# Aliases for backward compatibility.

# general
KEY_GENERAL_ARCHITECTURE         = Keys.General.ARCHITECTURE
KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION
KEY_GENERAL_ALIGNMENT            = Keys.General.ALIGNMENT
KEY_GENERAL_NAME                 = Keys.General.NAME
KEY_GENERAL_AUTHOR               = Keys.General.AUTHOR
KEY_GENERAL_URL                  = Keys.General.URL
KEY_GENERAL_DESCRIPTION          = Keys.General.DESCRIPTION
KEY_GENERAL_LICENSE              = Keys.General.LICENSE
KEY_GENERAL_SOURCE_URL           = Keys.General.SOURCE_URL
KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE

# LLM
KEY_VOCAB_SIZE            = Keys.LLM.VOCAB_SIZE
KEY_CONTEXT_LENGTH        = Keys.LLM.CONTEXT_LENGTH
KEY_EMBEDDING_LENGTH      = Keys.LLM.EMBEDDING_LENGTH
KEY_BLOCK_COUNT           = Keys.LLM.BLOCK_COUNT
KEY_FEED_FORWARD_LENGTH   = Keys.LLM.FEED_FORWARD_LENGTH
KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL
KEY_TENSOR_DATA_LAYOUT    = Keys.LLM.TENSOR_DATA_LAYOUT

# attention
KEY_ATTENTION_HEAD_COUNT        = Keys.Attention.HEAD_COUNT
KEY_ATTENTION_HEAD_COUNT_KV     = Keys.Attention.HEAD_COUNT_KV
KEY_ATTENTION_MAX_ALIBI_BIAS    = Keys.Attention.MAX_ALIBI_BIAS
KEY_ATTENTION_CLAMP_KQV         = Keys.Attention.CLAMP_KQV
KEY_ATTENTION_LAYERNORM_EPS     = Keys.Attention.LAYERNORM_EPS
KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS

# RoPE
KEY_ROPE_DIMENSION_COUNT      = Keys.Rope.DIMENSION_COUNT
KEY_ROPE_FREQ_BASE            = Keys.Rope.FREQ_BASE
KEY_ROPE_SCALING_TYPE         = Keys.Rope.SCALING_TYPE
KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED

# SSM
KEY_SSM_CONV_KERNEL    = Keys.SSM.CONV_KERNEL
KEY_SSM_INNER_SIZE     = Keys.SSM.INNER_SIZE
KEY_SSM_STATE_SIZE     = Keys.SSM.STATE_SIZE
KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK

# tokenization
KEY_TOKENIZER_MODEL      = Keys.Tokenizer.MODEL
KEY_TOKENIZER_LIST       = Keys.Tokenizer.LIST
KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE
KEY_TOKENIZER_SCORES     = Keys.Tokenizer.SCORES
KEY_TOKENIZER_MERGES     = Keys.Tokenizer.MERGES
KEY_TOKENIZER_BOS_ID     = Keys.Tokenizer.BOS_ID
KEY_TOKENIZER_EOS_ID     = Keys.Tokenizer.EOS_ID
KEY_TOKENIZER_UNK_ID     = Keys.Tokenizer.UNK_ID
KEY_TOKENIZER_SEP_ID     = Keys.Tokenizer.SEP_ID
KEY_TOKENIZER_PAD_ID     = Keys.Tokenizer.PAD_ID
KEY_TOKENIZER_CLS_ID     = Keys.Tokenizer.CLS_ID
KEY_TOKENIZER_MASK_ID    = Keys.Tokenizer.MASK_ID
KEY_TOKENIZER_HF_JSON    = Keys.Tokenizer.HF_JSON
KEY_TOKENIZER_RWKV       = Keys.Tokenizer.RWKV