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: 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" 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" 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 Tokenizer: MODEL = "tokenizer.ggml.model" LIST = "tokenizer.ggml.tokens" TOKEN_TYPE = "tokenizer.ggml.token_type" 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" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" 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() BLOOM = auto() STABLELM = auto() QWEN = auto() QWEN2 = auto() PHI2 = auto() PLAMO = auto() CODESHELL = auto() ORION = 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_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() 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.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", } 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.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_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_TYPES, MODEL_TENSOR.POS_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_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], 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, ], # 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, ], } # # 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 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 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), } # 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_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 # 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_HF_JSON = Keys.Tokenizer.HF_JSON KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV