mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 03:44:35 +00:00
a94e6ff877
* update: convert-hf-to-gguf.py to support Qwen2-57B-A14B * fix: QWEN2MOE support for expert_feed_forward_length previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH n_ff_exp and n_ff_shared_exp are now properly calculated * update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM * fix: QWEN2MOE support for expert_feed_forward_length previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH n_ff_exp and n_ff_shexp are now properly calculated
1089 lines
36 KiB
Python
1089 lines
36 KiB
Python
from __future__ import annotations
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from enum import Enum, IntEnum, auto
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from typing import Any
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#
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# constants
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#
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GGUF_MAGIC = 0x46554747 # "GGUF"
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GGUF_VERSION = 3
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GGUF_DEFAULT_ALIGNMENT = 32
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GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h
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#
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# metadata keys
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#
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class Keys:
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class General:
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ARCHITECTURE = "general.architecture"
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QUANTIZATION_VERSION = "general.quantization_version"
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ALIGNMENT = "general.alignment"
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NAME = "general.name"
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AUTHOR = "general.author"
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VERSION = "general.version"
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URL = "general.url"
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DESCRIPTION = "general.description"
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LICENSE = "general.license"
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SOURCE_URL = "general.source.url"
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SOURCE_HF_REPO = "general.source.huggingface.repository"
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FILE_TYPE = "general.file_type"
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class LLM:
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VOCAB_SIZE = "{arch}.vocab_size"
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CONTEXT_LENGTH = "{arch}.context_length"
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EMBEDDING_LENGTH = "{arch}.embedding_length"
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BLOCK_COUNT = "{arch}.block_count"
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LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count"
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FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
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EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length"
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EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length"
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USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
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TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
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EXPERT_COUNT = "{arch}.expert_count"
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EXPERT_USED_COUNT = "{arch}.expert_used_count"
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EXPERT_SHARED_COUNT = "{arch}.expert_shared_count"
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EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
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POOLING_TYPE = "{arch}.pooling_type"
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LOGIT_SCALE = "{arch}.logit_scale"
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class Attention:
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HEAD_COUNT = "{arch}.attention.head_count"
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HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
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MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
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CLAMP_KQV = "{arch}.attention.clamp_kqv"
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KEY_LENGTH = "{arch}.attention.key_length"
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VALUE_LENGTH = "{arch}.attention.value_length"
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LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
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CAUSAL = "{arch}.attention.causal"
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Q_LORA_RANK = "{arch}.attention.q_lora_rank"
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KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
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class Rope:
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DIMENSION_COUNT = "{arch}.rope.dimension_count"
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FREQ_BASE = "{arch}.rope.freq_base"
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SCALING_TYPE = "{arch}.rope.scaling.type"
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SCALING_FACTOR = "{arch}.rope.scaling.factor"
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SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor"
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SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
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SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
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SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier"
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class SSM:
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CONV_KERNEL = "{arch}.ssm.conv_kernel"
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INNER_SIZE = "{arch}.ssm.inner_size"
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STATE_SIZE = "{arch}.ssm.state_size"
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TIME_STEP_RANK = "{arch}.ssm.time_step_rank"
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class Tokenizer:
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MODEL = "tokenizer.ggml.model"
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PRE = "tokenizer.ggml.pre"
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LIST = "tokenizer.ggml.tokens"
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TOKEN_TYPE = "tokenizer.ggml.token_type"
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TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count" # for BERT-style token types
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SCORES = "tokenizer.ggml.scores"
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MERGES = "tokenizer.ggml.merges"
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BOS_ID = "tokenizer.ggml.bos_token_id"
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EOS_ID = "tokenizer.ggml.eos_token_id"
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UNK_ID = "tokenizer.ggml.unknown_token_id"
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SEP_ID = "tokenizer.ggml.seperator_token_id"
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PAD_ID = "tokenizer.ggml.padding_token_id"
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CLS_ID = "tokenizer.ggml.cls_token_id"
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MASK_ID = "tokenizer.ggml.mask_token_id"
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ADD_BOS = "tokenizer.ggml.add_bos_token"
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ADD_EOS = "tokenizer.ggml.add_eos_token"
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ADD_PREFIX = "tokenizer.ggml.add_space_prefix"
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HF_JSON = "tokenizer.huggingface.json"
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RWKV = "tokenizer.rwkv.world"
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CHAT_TEMPLATE = "tokenizer.chat_template"
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CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}"
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CHAT_TEMPLATES = "tokenizer.chat_templates"
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# FIM/Infill special tokens constants
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PREFIX_ID = "tokenizer.ggml.prefix_token_id"
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SUFFIX_ID = "tokenizer.ggml.suffix_token_id"
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MIDDLE_ID = "tokenizer.ggml.middle_token_id"
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EOT_ID = "tokenizer.ggml.eot_token_id"
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#
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# recommended mapping of model tensor names for storage in gguf
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#
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class MODEL_ARCH(IntEnum):
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LLAMA = auto()
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FALCON = auto()
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BAICHUAN = auto()
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GROK = auto()
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GPT2 = auto()
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GPTJ = auto()
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GPTNEOX = auto()
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MPT = auto()
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STARCODER = auto()
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REFACT = auto()
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BERT = auto()
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NOMIC_BERT = auto()
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JINA_BERT_V2 = auto()
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BLOOM = auto()
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STABLELM = auto()
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QWEN = auto()
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QWEN2 = auto()
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QWEN2MOE = auto()
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PHI2 = auto()
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PHI3 = auto()
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PLAMO = auto()
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CODESHELL = auto()
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ORION = auto()
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INTERNLM2 = auto()
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MINICPM = auto()
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GEMMA = auto()
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STARCODER2 = auto()
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MAMBA = auto()
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XVERSE = auto()
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COMMAND_R = auto()
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DBRX = auto()
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OLMO = auto()
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ARCTIC = auto()
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DEEPSEEK2 = auto()
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class MODEL_TENSOR(IntEnum):
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TOKEN_EMBD = auto()
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TOKEN_EMBD_NORM = auto()
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TOKEN_TYPES = auto()
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POS_EMBD = auto()
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OUTPUT = auto()
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OUTPUT_NORM = auto()
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ROPE_FREQS = auto()
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ROPE_FACTORS_LONG = auto()
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ROPE_FACTORS_SHORT = auto()
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ATTN_Q = auto()
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ATTN_K = auto()
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ATTN_V = auto()
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ATTN_QKV = auto()
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ATTN_OUT = auto()
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ATTN_NORM = auto()
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ATTN_NORM_2 = auto()
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ATTN_OUT_NORM = auto()
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ATTN_ROT_EMBD = auto()
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FFN_GATE_INP = auto()
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FFN_GATE_INP_SHEXP = auto()
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FFN_NORM = auto()
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FFN_GATE = auto()
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FFN_DOWN = auto()
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FFN_UP = auto()
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FFN_ACT = auto()
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FFN_NORM_EXP = auto()
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FFN_GATE_EXP = auto()
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FFN_DOWN_EXP = auto()
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FFN_UP_EXP = auto()
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FFN_GATE_SHEXP = auto()
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FFN_DOWN_SHEXP = auto()
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FFN_UP_SHEXP = auto()
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ATTN_Q_NORM = auto()
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ATTN_K_NORM = auto()
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LAYER_OUT_NORM = auto()
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SSM_IN = auto()
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SSM_CONV1D = auto()
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SSM_X = auto()
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SSM_DT = auto()
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SSM_A = auto()
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SSM_D = auto()
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SSM_OUT = auto()
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ATTN_Q_A = auto()
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ATTN_Q_B = auto()
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ATTN_KV_A_MQA = auto()
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ATTN_KV_B = auto()
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ATTN_Q_A_NORM = auto()
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ATTN_KV_A_NORM = auto()
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.BAICHUAN: "baichuan",
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MODEL_ARCH.GROK: "grok",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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MODEL_ARCH.STARCODER: "starcoder",
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MODEL_ARCH.REFACT: "refact",
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MODEL_ARCH.BERT: "bert",
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MODEL_ARCH.NOMIC_BERT: "nomic-bert",
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MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
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MODEL_ARCH.BLOOM: "bloom",
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MODEL_ARCH.STABLELM: "stablelm",
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MODEL_ARCH.QWEN: "qwen",
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MODEL_ARCH.QWEN2: "qwen2",
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MODEL_ARCH.QWEN2MOE: "qwen2moe",
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MODEL_ARCH.PHI2: "phi2",
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MODEL_ARCH.PHI3: "phi3",
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MODEL_ARCH.PLAMO: "plamo",
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MODEL_ARCH.CODESHELL: "codeshell",
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MODEL_ARCH.ORION: "orion",
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MODEL_ARCH.INTERNLM2: "internlm2",
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MODEL_ARCH.MINICPM: "minicpm",
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.ARCTIC: "arctic",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
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MODEL_TENSOR.TOKEN_TYPES: "token_types",
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MODEL_TENSOR.POS_EMBD: "position_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
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MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long",
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MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
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MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
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MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
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MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
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MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
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MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm",
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MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm",
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MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm",
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MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp",
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MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp",
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MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp",
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MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp",
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MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp",
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MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn",
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MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps",
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MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
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MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
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MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
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MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
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MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
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MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
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MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x",
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MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt",
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MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
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MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
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MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
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MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a",
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MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b",
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MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa",
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MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b",
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MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm",
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MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
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}
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MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_ARCH.LLAMA: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_GATE_INP,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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],
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MODEL_ARCH.GROK: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.ATTN_OUT_NORM,
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MODEL_TENSOR.FFN_GATE_INP,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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MODEL_TENSOR.LAYER_OUT_NORM,
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],
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MODEL_ARCH.GPTNEOX: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_QKV,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.FALCON: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_NORM_2,
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MODEL_TENSOR.ATTN_QKV,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.BAICHUAN: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.STARCODER: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.POS_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_QKV,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
|
|
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.JINA_BERT_V2: [
|
|
MODEL_TENSOR.TOKEN_EMBD,
|
|
MODEL_TENSOR.TOKEN_EMBD_NORM,
|
|
MODEL_TENSOR.TOKEN_TYPES,
|
|
MODEL_TENSOR.ATTN_NORM_2,
|
|
MODEL_TENSOR.ATTN_OUT_NORM,
|
|
MODEL_TENSOR.ATTN_Q,
|
|
MODEL_TENSOR.ATTN_Q_NORM,
|
|
MODEL_TENSOR.ATTN_K,
|
|
MODEL_TENSOR.ATTN_K_NORM,
|
|
MODEL_TENSOR.ATTN_V,
|
|
MODEL_TENSOR.ATTN_OUT,
|
|
MODEL_TENSOR.FFN_UP,
|
|
MODEL_TENSOR.FFN_GATE,
|
|
MODEL_TENSOR.FFN_DOWN,
|
|
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_TENSOR.ATTN_Q_NORM,
|
|
MODEL_TENSOR.ATTN_K_NORM,
|
|
MODEL_TENSOR.POS_EMBD,
|
|
],
|
|
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.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_TENSOR.ATTN_Q_NORM,
|
|
MODEL_TENSOR.ATTN_K_NORM,
|
|
],
|
|
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.QWEN2MOE: [
|
|
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_INP,
|
|
MODEL_TENSOR.FFN_GATE_EXP,
|
|
MODEL_TENSOR.FFN_DOWN_EXP,
|
|
MODEL_TENSOR.FFN_UP_EXP,
|
|
MODEL_TENSOR.FFN_GATE_INP_SHEXP,
|
|
MODEL_TENSOR.FFN_GATE_SHEXP,
|
|
MODEL_TENSOR.FFN_DOWN_SHEXP,
|
|
MODEL_TENSOR.FFN_UP_SHEXP,
|
|
],
|
|
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.PHI3: [
|
|
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,
|
|
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,
|
|
],
|
|
MODEL_ARCH.XVERSE: [
|
|
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.COMMAND_R: [
|
|
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.ATTN_K_NORM,
|
|
MODEL_TENSOR.ATTN_Q_NORM,
|
|
],
|
|
MODEL_ARCH.DBRX: [
|
|
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.ATTN_OUT_NORM,
|
|
MODEL_TENSOR.FFN_GATE_INP,
|
|
MODEL_TENSOR.FFN_GATE_EXP,
|
|
MODEL_TENSOR.FFN_DOWN_EXP,
|
|
MODEL_TENSOR.FFN_UP_EXP,
|
|
],
|
|
MODEL_ARCH.OLMO: [
|
|
MODEL_TENSOR.TOKEN_EMBD,
|
|
MODEL_TENSOR.OUTPUT,
|
|
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_ARCH.ARCTIC: [
|
|
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_NORM_EXP,
|
|
MODEL_TENSOR.FFN_GATE_EXP,
|
|
MODEL_TENSOR.FFN_DOWN_EXP,
|
|
MODEL_TENSOR.FFN_UP_EXP,
|
|
],
|
|
MODEL_ARCH.DEEPSEEK2: [
|
|
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_Q_A,
|
|
MODEL_TENSOR.ATTN_Q_B,
|
|
MODEL_TENSOR.ATTN_KV_A_MQA,
|
|
MODEL_TENSOR.ATTN_KV_B,
|
|
MODEL_TENSOR.ATTN_Q_A_NORM,
|
|
MODEL_TENSOR.ATTN_KV_A_NORM,
|
|
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_TENSOR.FFN_GATE_SHEXP,
|
|
MODEL_TENSOR.FFN_DOWN_SHEXP,
|
|
MODEL_TENSOR.FFN_UP_SHEXP,
|
|
],
|
|
# 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.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,
|
|
],
|
|
MODEL_ARCH.XVERSE: [
|
|
MODEL_TENSOR.ROPE_FREQS,
|
|
MODEL_TENSOR.ATTN_ROT_EMBD,
|
|
],
|
|
MODEL_ARCH.DEEPSEEK2: [
|
|
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
|
|
IQ1_M = 29
|
|
BF16 = 30
|
|
|
|
|
|
# TODO: add GGMLFileType from ggml_ftype in ggml.h
|
|
|
|
|
|
# from llama_ftype in llama.h
|
|
# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE.
|
|
class LlamaFileType(IntEnum):
|
|
ALL_F32 = 0
|
|
MOSTLY_F16 = 1 # except 1d tensors
|
|
MOSTLY_Q4_0 = 2 # except 1d tensors
|
|
MOSTLY_Q4_1 = 3 # except 1d tensors
|
|
MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16
|
|
# MOSTLY_Q4_2 = 5 # support has been removed
|
|
# MOSTLY_Q4_3 = 6 # support has been removed
|
|
MOSTLY_Q8_0 = 7 # except 1d tensors
|
|
MOSTLY_Q5_0 = 8 # except 1d tensors
|
|
MOSTLY_Q5_1 = 9 # except 1d tensors
|
|
MOSTLY_Q2_K = 10 # except 1d tensors
|
|
MOSTLY_Q3_K_S = 11 # except 1d tensors
|
|
MOSTLY_Q3_K_M = 12 # except 1d tensors
|
|
MOSTLY_Q3_K_L = 13 # except 1d tensors
|
|
MOSTLY_Q4_K_S = 14 # except 1d tensors
|
|
MOSTLY_Q4_K_M = 15 # except 1d tensors
|
|
MOSTLY_Q5_K_S = 16 # except 1d tensors
|
|
MOSTLY_Q5_K_M = 17 # except 1d tensors
|
|
MOSTLY_Q6_K = 18 # except 1d tensors
|
|
MOSTLY_IQ2_XXS = 19 # except 1d tensors
|
|
MOSTLY_IQ2_XS = 20 # except 1d tensors
|
|
MOSTLY_Q2_K_S = 21 # except 1d tensors
|
|
MOSTLY_IQ3_XS = 22 # except 1d tensors
|
|
MOSTLY_IQ3_XXS = 23 # except 1d tensors
|
|
MOSTLY_IQ1_S = 24 # except 1d tensors
|
|
MOSTLY_IQ4_NL = 25 # except 1d tensors
|
|
MOSTLY_IQ3_S = 26 # except 1d tensors
|
|
MOSTLY_IQ3_M = 27 # except 1d tensors
|
|
MOSTLY_IQ2_S = 28 # except 1d tensors
|
|
MOSTLY_IQ2_M = 29 # except 1d tensors
|
|
MOSTLY_IQ4_XS = 30 # except 1d tensors
|
|
MOSTLY_IQ1_M = 31 # except 1d tensors
|
|
MOSTLY_BF16 = 32 # except 1d tensors
|
|
|
|
GUESSED = 1024 # not specified in the model file
|
|
|
|
|
|
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:
|
|
raise ValueError(f"Unknown type: {type(val)}")
|
|
|
|
|
|
# Items here are (block size, type size)
|
|
QK_K = 256
|
|
GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
|
|
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),
|
|
GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32),
|
|
GGMLQuantizationType.BF16: (1, 2),
|
|
}
|
|
|
|
|
|
# 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_PRE = Keys.Tokenizer.PRE
|
|
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
|
|
KEY_TOKENIZER_PRIFIX_ID = Keys.Tokenizer.PREFIX_ID
|
|
KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID
|
|
KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID
|
|
KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID
|