mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
8cf19d60dc
* check whether platform is 390x if yes->do not import immintrin.h * support s390x big endian * support --bigendian option for s390x 1. verified with baichuan7b-chat with float 16 on s390x 2. verified with baichuan7b-chat 3. verified with chinese-alpaca-2-13b-f16 * update format based on editor-config checker result * Update convert-baichuan-hf-to-gguf.py * 1. check in ggml.c if endianess is not match 2. update GGUF version 3. change get_pack_prefix to property 4. update information log * always use "GGUF" as beginng of GGUF file * Compare "GGUF" with file header char by char 1. Set GGUF_MAGIC to "GGUF" string instead of int value 2. Compare "GGUF" char by char to ensure its byte order 3. Move bytes swap code from convert.py to gguf.py write_tensor_data --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
1090 lines
39 KiB
Python
1090 lines
39 KiB
Python
#!/usr/bin/env python3
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import os
|
|
import shutil
|
|
import struct
|
|
import sys
|
|
import tempfile
|
|
from enum import IntEnum, auto
|
|
from io import BufferedWriter
|
|
from pathlib import Path
|
|
from typing import IO, Any, BinaryIO, Callable, Sequence
|
|
|
|
import numpy as np
|
|
|
|
#
|
|
# constants
|
|
#
|
|
|
|
GGUF_MAGIC = 0x46554747
|
|
GGUF_VERSION = 3
|
|
GGUF_DEFAULT_ALIGNMENT = 32
|
|
|
|
|
|
# general
|
|
KEY_GENERAL_ARCHITECTURE = "general.architecture"
|
|
KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
|
|
KEY_GENERAL_ALIGNMENT = "general.alignment"
|
|
KEY_GENERAL_NAME = "general.name"
|
|
KEY_GENERAL_AUTHOR = "general.author"
|
|
KEY_GENERAL_URL = "general.url"
|
|
KEY_GENERAL_DESCRIPTION = "general.description"
|
|
KEY_GENERAL_LICENSE = "general.license"
|
|
KEY_GENERAL_SOURCE_URL = "general.source.url"
|
|
KEY_GENERAL_SOURCE_HF_REPO = "general.source.huggingface.repository"
|
|
KEY_GENERAL_FILE_TYPE = "general.file_type"
|
|
|
|
# LLM
|
|
KEY_CONTEXT_LENGTH = "{arch}.context_length"
|
|
KEY_EMBEDDING_LENGTH = "{arch}.embedding_length"
|
|
KEY_BLOCK_COUNT = "{arch}.block_count"
|
|
KEY_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
|
|
KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
|
|
KEY_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
|
|
|
|
# attention
|
|
KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
|
|
KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
|
|
KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
|
|
KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
|
|
KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
|
|
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
|
|
|
|
# RoPE
|
|
KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
|
|
KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base"
|
|
KEY_ROPE_SCALE_LINEAR = "{arch}.rope.scale_linear"
|
|
|
|
# tokenization
|
|
KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
|
|
KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
|
|
KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
|
|
KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores"
|
|
KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges"
|
|
KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"
|
|
KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"
|
|
KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"
|
|
KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"
|
|
KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"
|
|
KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json"
|
|
KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
|
|
|
|
|
|
#
|
|
# recommended mapping of model tensor names for storage in gguf
|
|
#
|
|
|
|
|
|
class MODEL_ARCH(IntEnum):
|
|
LLAMA : int = auto()
|
|
FALCON : int = auto()
|
|
BAICHUAN : int = auto()
|
|
GPT2 : int = auto()
|
|
GPTJ : int = auto()
|
|
GPTNEOX : int = auto()
|
|
MPT : int = auto()
|
|
STARCODER : int = auto()
|
|
PERSIMMON : int = auto()
|
|
REFACT : int = auto()
|
|
BERT : int = auto()
|
|
BLOOM : int = auto()
|
|
|
|
|
|
class MODEL_TENSOR(IntEnum):
|
|
TOKEN_EMBD : int = auto()
|
|
TOKEN_EMBD_NORM : int = auto()
|
|
TOKEN_TYPES : int = auto()
|
|
POS_EMBD : int = auto()
|
|
OUTPUT : int = auto()
|
|
OUTPUT_NORM : int = auto()
|
|
ROPE_FREQS : int = auto()
|
|
ATTN_Q : int = auto()
|
|
ATTN_K : int = auto()
|
|
ATTN_V : int = auto()
|
|
ATTN_QKV : int = auto()
|
|
ATTN_OUT : int = auto()
|
|
ATTN_NORM : int = auto()
|
|
ATTN_NORM_2 : int = auto()
|
|
ATTN_ROT_EMBD : int = auto()
|
|
FFN_GATE : int = auto()
|
|
FFN_DOWN : int = auto()
|
|
FFN_UP : int = auto()
|
|
FFN_NORM : int = auto()
|
|
ATTN_Q_NORM : int = auto()
|
|
ATTN_K_NORM : int = 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",
|
|
}
|
|
|
|
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_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_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_NORM,
|
|
MODEL_TENSOR.FFN_GATE,
|
|
MODEL_TENSOR.FFN_DOWN,
|
|
MODEL_TENSOR.FFN_UP,
|
|
],
|
|
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_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.GPT2: [
|
|
# TODO
|
|
],
|
|
# 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,
|
|
]
|
|
}
|
|
|
|
|
|
class TensorNameMap:
|
|
mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
|
# Token embeddings
|
|
MODEL_TENSOR.TOKEN_EMBD: (
|
|
"gpt_neox.embed_in", # gptneox
|
|
"transformer.wte", # gpt2 gpt-j mpt refact
|
|
"transformer.word_embeddings", # falcon
|
|
"word_embeddings", # bloom
|
|
"model.embed_tokens", # llama-hf
|
|
"tok_embeddings", # llama-pth
|
|
"embeddings.word_embeddings", # bert
|
|
"language_model.embedding.word_embeddings", # persimmon
|
|
),
|
|
|
|
# Token type embeddings
|
|
MODEL_TENSOR.TOKEN_TYPES: (
|
|
"embeddings.token_type_embeddings", # bert
|
|
),
|
|
|
|
# Normalization of token embeddings
|
|
MODEL_TENSOR.TOKEN_EMBD_NORM: (
|
|
"word_embeddings_layernorm", # bloom
|
|
),
|
|
|
|
# Position embeddings
|
|
MODEL_TENSOR.POS_EMBD: (
|
|
"transformer.wpe", # gpt2
|
|
"embeddings.position_embeddings", # bert
|
|
),
|
|
|
|
# Output
|
|
MODEL_TENSOR.OUTPUT: (
|
|
"embed_out", # gptneox
|
|
"lm_head", # gpt2 mpt falcon llama-hf baichuan
|
|
"output", # llama-pth bloom
|
|
"word_embeddings_for_head", # persimmon
|
|
),
|
|
|
|
# Output norm
|
|
MODEL_TENSOR.OUTPUT_NORM: (
|
|
"gpt_neox.final_layer_norm", # gptneox
|
|
"transformer.ln_f", # gpt2 gpt-j falcon
|
|
"model.norm", # llama-hf baichuan
|
|
"norm", # llama-pth
|
|
"embeddings.LayerNorm", # bert
|
|
"transformer.norm_f", # mpt
|
|
"ln_f", # refact bloom
|
|
"language_model.encoder.final_layernorm", # persimmon
|
|
),
|
|
|
|
# Rope frequencies
|
|
MODEL_TENSOR.ROPE_FREQS: (
|
|
"rope.freqs", # llama-pth
|
|
),
|
|
}
|
|
|
|
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
|
# Attention norm
|
|
MODEL_TENSOR.ATTN_NORM: (
|
|
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
|
|
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
|
|
"transformer.blocks.{bid}.norm_1", # mpt
|
|
"transformer.h.{bid}.input_layernorm", # falcon7b
|
|
"h.{bid}.input_layernorm", # bloom
|
|
"transformer.h.{bid}.ln_mlp", # falcon40b
|
|
"model.layers.{bid}.input_layernorm", # llama-hf
|
|
"layers.{bid}.attention_norm", # llama-pth
|
|
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
|
|
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
|
|
),
|
|
|
|
# Attention norm 2
|
|
MODEL_TENSOR.ATTN_NORM_2: (
|
|
"transformer.h.{bid}.ln_attn", # falcon40b
|
|
),
|
|
|
|
# Attention query-key-value
|
|
MODEL_TENSOR.ATTN_QKV: (
|
|
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
|
|
"transformer.h.{bid}.attn.c_attn", # gpt2
|
|
"transformer.blocks.{bid}.attn.Wqkv", # mpt
|
|
"transformer.h.{bid}.self_attention.query_key_value", # falcon
|
|
"h.{bid}.self_attention.query_key_value", # bloom
|
|
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
|
|
),
|
|
|
|
# Attention query
|
|
MODEL_TENSOR.ATTN_Q: (
|
|
"model.layers.{bid}.self_attn.q_proj", # llama-hf
|
|
"layers.{bid}.attention.wq", # llama-pth
|
|
"encoder.layer.{bid}.attention.self.query", # bert
|
|
"transformer.h.{bid}.attn.q_proj", # gpt-j
|
|
),
|
|
|
|
# Attention key
|
|
MODEL_TENSOR.ATTN_K: (
|
|
"model.layers.{bid}.self_attn.k_proj", # llama-hf
|
|
"layers.{bid}.attention.wk", # llama-pth
|
|
"encoder.layer.{bid}.attention.self.key", # bert
|
|
"transformer.h.{bid}.attn.k_proj", # gpt-j
|
|
),
|
|
|
|
# Attention value
|
|
MODEL_TENSOR.ATTN_V: (
|
|
"model.layers.{bid}.self_attn.v_proj", # llama-hf
|
|
"layers.{bid}.attention.wv", # llama-pth
|
|
"encoder.layer.{bid}.attention.self.value", # bert
|
|
"transformer.h.{bid}.attn.v_proj", # gpt-j
|
|
),
|
|
|
|
# Attention output
|
|
MODEL_TENSOR.ATTN_OUT: (
|
|
"gpt_neox.layers.{bid}.attention.dense", # gptneox
|
|
"transformer.h.{bid}.attn.c_proj", # gpt2 refact
|
|
"transformer.blocks.{bid}.attn.out_proj", # mpt
|
|
"transformer.h.{bid}.self_attention.dense", # falcon
|
|
"h.{bid}.self_attention.dense", # bloom
|
|
"model.layers.{bid}.self_attn.o_proj", # llama-hf
|
|
"layers.{bid}.attention.wo", # llama-pth
|
|
"encoder.layer.{bid}.attention.output.dense", # bert
|
|
"transformer.h.{bid}.attn.out_proj", # gpt-j
|
|
"language_model.encoder.layers.{bid}.self_attention.dense" # persimmon
|
|
),
|
|
|
|
# Rotary embeddings
|
|
MODEL_TENSOR.ATTN_ROT_EMBD: (
|
|
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
|
|
"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
|
|
),
|
|
|
|
# Feed-forward norm
|
|
MODEL_TENSOR.FFN_NORM: (
|
|
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
|
|
"transformer.h.{bid}.ln_2", # gpt2 refact
|
|
"h.{bid}.post_attention_layernorm", # bloom
|
|
"transformer.blocks.{bid}.norm_2", # mpt
|
|
"model.layers.{bid}.post_attention_layernorm", # llama-hf
|
|
"layers.{bid}.ffn_norm", # llama-pth
|
|
"encoder.layer.{bid}.output.LayerNorm", # bert
|
|
"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
|
|
),
|
|
|
|
# Feed-forward up
|
|
MODEL_TENSOR.FFN_UP: (
|
|
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
|
|
"transformer.h.{bid}.mlp.c_fc", # gpt2
|
|
"transformer.blocks.{bid}.ffn.up_proj", # mpt
|
|
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
|
|
"h.{bid}.mlp.dense_h_to_4h", # bloom
|
|
"model.layers.{bid}.mlp.up_proj", # llama-hf refact
|
|
"layers.{bid}.feed_forward.w3", # llama-pth
|
|
"encoder.layer.{bid}.intermediate.dense", # bert
|
|
"transformer.h.{bid}.mlp.fc_in", # gpt-j
|
|
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
|
|
),
|
|
|
|
# Feed-forward gate
|
|
MODEL_TENSOR.FFN_GATE: (
|
|
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
|
|
"layers.{bid}.feed_forward.w1", # llama-pth
|
|
),
|
|
|
|
# Feed-forward down
|
|
MODEL_TENSOR.FFN_DOWN: (
|
|
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
|
|
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact
|
|
"transformer.blocks.{bid}.ffn.down_proj", # mpt
|
|
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
|
|
"h.{bid}.mlp.dense_4h_to_h", # bloom
|
|
"model.layers.{bid}.mlp.down_proj", # llama-hf
|
|
"layers.{bid}.feed_forward.w2", # llama-pth
|
|
"encoder.layer.{bid}.output.dense", # bert
|
|
"transformer.h.{bid}.mlp.fc_out", # gpt-j
|
|
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
|
|
),
|
|
|
|
MODEL_TENSOR.ATTN_Q_NORM: (
|
|
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
|
|
),
|
|
|
|
MODEL_TENSOR.ATTN_K_NORM: (
|
|
"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
|
|
),
|
|
|
|
MODEL_TENSOR.ROPE_FREQS: (
|
|
"language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon
|
|
)
|
|
}
|
|
|
|
mapping: dict[str, tuple[MODEL_TENSOR, str]]
|
|
|
|
def __init__(self, arch: MODEL_ARCH, n_blocks: int):
|
|
self.mapping = {}
|
|
for tensor, keys in self.mappings_cfg.items():
|
|
if tensor not in MODEL_TENSORS[arch]:
|
|
continue
|
|
tensor_name = TENSOR_NAMES[tensor]
|
|
self.mapping[tensor_name] = (tensor, tensor_name)
|
|
for key in keys:
|
|
self.mapping[key] = (tensor, tensor_name)
|
|
for bid in range(n_blocks):
|
|
for tensor, keys in self.block_mappings_cfg.items():
|
|
if tensor not in MODEL_TENSORS[arch]:
|
|
continue
|
|
tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
|
|
self.mapping[tensor_name] = (tensor, tensor_name)
|
|
for key in keys:
|
|
key = key.format(bid = bid)
|
|
self.mapping[key] = (tensor, tensor_name)
|
|
|
|
def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
|
|
result = self.mapping.get(key)
|
|
if result is not None:
|
|
return result
|
|
for suffix in try_suffixes:
|
|
if key.endswith(suffix):
|
|
result = self.mapping.get(key[:-len(suffix)])
|
|
if result is not None:
|
|
return (result[0], result[1] + suffix)
|
|
return None
|
|
|
|
def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None:
|
|
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
|
|
if result is None:
|
|
return None
|
|
return result[1]
|
|
|
|
def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None:
|
|
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
|
|
if result is None:
|
|
return None
|
|
return result[0]
|
|
|
|
def __getitem__(self, key: str) -> str:
|
|
try:
|
|
return self.mapping[key][1]
|
|
except KeyError:
|
|
raise KeyError(key)
|
|
|
|
def __contains__(self, key: str) -> bool:
|
|
return key in self.mapping
|
|
|
|
def __repr__(self) -> str:
|
|
return repr(self.mapping)
|
|
|
|
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap:
|
|
return TensorNameMap(arch, n_blocks)
|
|
|
|
class TokenType(IntEnum):
|
|
NORMAL = 1
|
|
UNKNOWN = 2
|
|
CONTROL = 3
|
|
USER_DEFINED = 4
|
|
UNUSED = 5
|
|
BYTE = 6
|
|
|
|
#
|
|
# implementation
|
|
#
|
|
|
|
|
|
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):
|
|
if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, 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: "+str(type(val)))
|
|
sys.exit()
|
|
|
|
|
|
class GGUFWriter:
|
|
fout: BufferedWriter
|
|
arch: str
|
|
offset_tensor = 0
|
|
data_alignment = GGUF_DEFAULT_ALIGNMENT
|
|
kv_data = b""
|
|
kv_data_count = 0
|
|
ti_data = b""
|
|
ti_data_count = 0
|
|
use_temp_file: bool
|
|
temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None
|
|
tensors: list[tuple[np.ndarray[Any, Any], int]]
|
|
|
|
@property
|
|
def pack_prefix(self):
|
|
if self.endianess==GGUFEndian.LITTLE:
|
|
return "<"
|
|
else:
|
|
return ">"
|
|
|
|
def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True, endianess=GGUFEndian.LITTLE):
|
|
self.fout = open(path, "wb")
|
|
self.arch = arch
|
|
self.endianess = endianess
|
|
self._simple_value_packing = {
|
|
GGUFValueType.UINT8: f"{self.pack_prefix}B",
|
|
GGUFValueType.INT8: f"{self.pack_prefix}b",
|
|
GGUFValueType.UINT16: f"{self.pack_prefix}H",
|
|
GGUFValueType.INT16: f"{self.pack_prefix}h",
|
|
GGUFValueType.UINT32: f"{self.pack_prefix}I",
|
|
GGUFValueType.INT32: f"{self.pack_prefix}i",
|
|
GGUFValueType.FLOAT32: f"{self.pack_prefix}f",
|
|
GGUFValueType.UINT64: f"{self.pack_prefix}Q",
|
|
GGUFValueType.INT64: f"{self.pack_prefix}q",
|
|
GGUFValueType.FLOAT64: f"{self.pack_prefix}d",
|
|
GGUFValueType.BOOL: "?" ,
|
|
}
|
|
self.add_architecture()
|
|
self.use_temp_file = use_temp_file
|
|
self.tensors = []
|
|
endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian"
|
|
print(f"This gguf file is for {endianess_str} only")
|
|
|
|
def write_header_to_file(self):
|
|
self.fout.write(struct.pack("<I", GGUF_MAGIC))
|
|
self.fout.write(struct.pack(f"{self.pack_prefix}I", GGUF_VERSION))
|
|
self.fout.write(struct.pack(f"{self.pack_prefix}Q", self.ti_data_count))
|
|
self.fout.write(struct.pack(f"{self.pack_prefix}Q", self.kv_data_count))
|
|
self.flush()
|
|
# print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
|
|
|
|
def write_kv_data_to_file(self):
|
|
self.fout.write(self.kv_data)
|
|
self.flush()
|
|
|
|
def write_ti_data_to_file(self):
|
|
self.fout.write(self.ti_data)
|
|
self.flush()
|
|
|
|
def add_key(self, key: str):
|
|
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
|
|
|
|
def add_uint8(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.UINT8)
|
|
|
|
def add_int8(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.INT8)
|
|
|
|
def add_uint16(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.UINT16)
|
|
|
|
def add_int16(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.INT16)
|
|
|
|
def add_uint32(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.UINT32)
|
|
|
|
def add_int32(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.INT32)
|
|
|
|
def add_float32(self, key: str, val: float):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.FLOAT32)
|
|
|
|
def add_uint64(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.UINT64)
|
|
|
|
def add_int64(self, key: str, val: int):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.INT64)
|
|
|
|
def add_float64(self, key: str, val: float):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.FLOAT64)
|
|
|
|
def add_bool(self, key: str, val: bool):
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.BOOL)
|
|
|
|
def add_string(self, key: str, val: str):
|
|
if len(val) == 0:
|
|
return
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.STRING)
|
|
|
|
def add_array(self, key: str, val: Sequence[Any]):
|
|
if not isinstance(val, Sequence):
|
|
raise ValueError("Value must be a sequence for array type")
|
|
|
|
self.add_key(key)
|
|
self.add_val(val, GGUFValueType.ARRAY)
|
|
|
|
def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True):
|
|
if vtype is None:
|
|
vtype = GGUFValueType.get_type(val)
|
|
|
|
if add_vtype:
|
|
self.kv_data += struct.pack(f"{self.pack_prefix}I", vtype)
|
|
self.kv_data_count += 1
|
|
|
|
pack_fmt = self._simple_value_packing.get(vtype)
|
|
if pack_fmt is not None:
|
|
self.kv_data += struct.pack(pack_fmt, val)
|
|
elif vtype == GGUFValueType.STRING:
|
|
encoded_val = val.encode("utf8") if isinstance(val, str) else val
|
|
self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_val))
|
|
self.kv_data += encoded_val
|
|
elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and len(val) > 0:
|
|
ltype = GGUFValueType.get_type(val[0])
|
|
if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
|
|
raise ValueError("All items in a GGUF array should be of the same type")
|
|
self.kv_data += struct.pack(f"{self.pack_prefix}I", ltype)
|
|
self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(val))
|
|
for item in val:
|
|
self.add_val(item, add_vtype=False)
|
|
else:
|
|
raise ValueError("Invalid GGUF metadata value type or value")
|
|
|
|
@staticmethod
|
|
def ggml_pad(x: int, n: int) -> int:
|
|
return ((x + n - 1) // n) * n
|
|
|
|
def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None):
|
|
assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
|
|
|
|
encoded_name = name.encode("utf8")
|
|
self.ti_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_name))
|
|
self.ti_data += encoded_name
|
|
n_dims = len(tensor_shape)
|
|
self.ti_data += struct.pack(f"{self.pack_prefix}I", n_dims)
|
|
for i in range(n_dims):
|
|
self.ti_data += struct.pack(f"{self.pack_prefix}Q", tensor_shape[n_dims - 1 - i])
|
|
if raw_dtype is None:
|
|
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
|
|
else:
|
|
dtype = raw_dtype
|
|
self.ti_data += struct.pack(f"{self.pack_prefix}I", dtype)
|
|
self.ti_data += struct.pack(f"{self.pack_prefix}Q", self.offset_tensor)
|
|
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
|
|
self.ti_data_count += 1
|
|
|
|
def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None):
|
|
if self.endianess == GGUFEndian.BIG:
|
|
tensor.byteswap(inplace=True)
|
|
if self.use_temp_file and self.temp_file is None:
|
|
fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
|
|
fp.seek(0)
|
|
self.temp_file = fp
|
|
|
|
shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
|
|
self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
|
|
|
|
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
|
|
|
|
if self.temp_file is None:
|
|
self.tensors.append((tensor, pad))
|
|
return
|
|
|
|
tensor.tofile(self.temp_file)
|
|
|
|
if pad != 0:
|
|
self.temp_file.write(bytes([0] * pad))
|
|
|
|
def write_padding(self, fp: BinaryIO, n: int, align: int | None = None):
|
|
pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
|
|
if pad != 0:
|
|
fp.write(bytes([0] * pad))
|
|
|
|
def write_tensor_data(self, tensor: np.ndarray[Any, Any]):
|
|
if self.endianess==GGUFEndian.BIG:
|
|
tensor.byteswap(inplace=True)
|
|
self.write_padding(self.fout, self.fout.tell())
|
|
tensor.tofile(self.fout)
|
|
self.write_padding(self.fout, tensor.nbytes)
|
|
|
|
def write_tensors_to_file(self):
|
|
self.write_ti_data_to_file()
|
|
|
|
self.write_padding(self.fout, self.fout.tell())
|
|
|
|
if self.temp_file is None:
|
|
for (currtensor, currpad) in self.tensors:
|
|
currtensor.tofile(self.fout)
|
|
if currpad != 0:
|
|
self.fout.write(bytes([0] * currpad))
|
|
return
|
|
|
|
self.temp_file.seek(0)
|
|
|
|
shutil.copyfileobj(self.temp_file, self.fout)
|
|
self.flush()
|
|
self.temp_file.close()
|
|
|
|
def flush(self):
|
|
self.fout.flush()
|
|
|
|
def close(self):
|
|
self.fout.close()
|
|
|
|
def add_architecture(self):
|
|
self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
|
|
|
|
def add_author(self, author: str):
|
|
self.add_string(KEY_GENERAL_AUTHOR, author)
|
|
|
|
def add_tensor_data_layout(self, layout: str):
|
|
self.add_string(KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
|
|
|
|
def add_url(self, url: str):
|
|
self.add_string(KEY_GENERAL_URL, url)
|
|
|
|
def add_description(self, description: str):
|
|
self.add_string(KEY_GENERAL_DESCRIPTION, description)
|
|
|
|
def add_source_url(self, url: str):
|
|
self.add_string(KEY_GENERAL_SOURCE_URL, url)
|
|
|
|
def add_source_hf_repo(self, repo: str):
|
|
self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
|
|
|
|
def add_file_type(self, ftype: int):
|
|
self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype)
|
|
|
|
def add_name(self, name: str):
|
|
self.add_string(KEY_GENERAL_NAME, name)
|
|
|
|
def add_quantization_version(self, quantization_version: GGMLQuantizationType):
|
|
self.add_uint32(
|
|
KEY_GENERAL_QUANTIZATION_VERSION, quantization_version)
|
|
|
|
def add_custom_alignment(self, alignment: int):
|
|
self.data_alignment = alignment
|
|
self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
|
|
|
|
def add_context_length(self, length: int):
|
|
self.add_uint32(
|
|
KEY_CONTEXT_LENGTH.format(arch=self.arch), length)
|
|
|
|
def add_embedding_length(self, length: int):
|
|
self.add_uint32(
|
|
KEY_EMBEDDING_LENGTH.format(arch=self.arch), length)
|
|
|
|
def add_block_count(self, length: int):
|
|
self.add_uint32(
|
|
KEY_BLOCK_COUNT.format(arch=self.arch), length)
|
|
|
|
def add_feed_forward_length(self, length: int):
|
|
self.add_uint32(
|
|
KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
|
|
|
|
def add_parallel_residual(self, use: bool):
|
|
self.add_bool(
|
|
KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
|
|
|
|
def add_head_count(self, count: int):
|
|
self.add_uint32(
|
|
KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
|
|
|
|
def add_head_count_kv(self, count: int):
|
|
self.add_uint32(
|
|
KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
|
|
|
|
def add_max_alibi_bias(self, bias: float):
|
|
self.add_float32(
|
|
KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
|
|
|
|
def add_clamp_kqv(self, value: float):
|
|
self.add_float32(
|
|
KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
|
|
|
|
def add_layer_norm_eps(self, value: float):
|
|
self.add_float32(
|
|
KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
|
|
|
|
def add_layer_norm_rms_eps(self, value: float):
|
|
self.add_float32(
|
|
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
|
|
|
|
def add_rope_dimension_count(self, count: int):
|
|
self.add_uint32(
|
|
KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
|
|
|
|
def add_rope_freq_base(self, value: float):
|
|
self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value)
|
|
|
|
def add_rope_scale_linear(self, value: float):
|
|
self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value)
|
|
|
|
def add_tokenizer_model(self, model: str):
|
|
self.add_string(KEY_TOKENIZER_MODEL, model)
|
|
|
|
def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]):
|
|
self.add_array(KEY_TOKENIZER_LIST, tokens)
|
|
|
|
def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]):
|
|
self.add_array(KEY_TOKENIZER_MERGES, merges)
|
|
|
|
def add_token_types(self, types: Sequence[TokenType] | Sequence[int]):
|
|
self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
|
|
|
|
def add_token_scores(self, scores: Sequence[float]):
|
|
self.add_array(KEY_TOKENIZER_SCORES, scores)
|
|
|
|
def add_bos_token_id(self, id: int):
|
|
self.add_uint32(KEY_TOKENIZER_BOS_ID, id)
|
|
|
|
def add_eos_token_id(self, id: int):
|
|
self.add_uint32(KEY_TOKENIZER_EOS_ID, id)
|
|
|
|
def add_unk_token_id(self, id: int):
|
|
self.add_uint32(KEY_TOKENIZER_UNK_ID, id)
|
|
|
|
def add_sep_token_id(self, id: int):
|
|
self.add_uint32(KEY_TOKENIZER_SEP_ID, id)
|
|
|
|
def add_pad_token_id(self, id: int):
|
|
self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
|
|
|
|
|
|
class SpecialVocab:
|
|
load_merges: bool = False
|
|
merges: list[str] = []
|
|
special_token_types: tuple[str, ...] = ('bos', 'eos', 'unk', 'sep', 'pad')
|
|
special_token_ids: dict[str, int] = {}
|
|
|
|
def __init__(
|
|
self, path: str | os.PathLike[str], load_merges: bool = False,
|
|
special_token_types: tuple[str, ...] | None = None,
|
|
):
|
|
self.special_token_ids = {}
|
|
self.load_merges = load_merges
|
|
if special_token_types is not None:
|
|
self.special_token_types = special_token_types
|
|
self._load(Path(path))
|
|
|
|
def _load(self, path: Path) -> None:
|
|
if not self._try_load_from_tokenizer_json(path):
|
|
self._try_load_from_config_json(path)
|
|
|
|
def _try_load_from_tokenizer_json(self, path: Path) -> bool:
|
|
tokenizer_file = path / 'tokenizer.json'
|
|
if not tokenizer_file.is_file():
|
|
return False
|
|
with open(tokenizer_file, encoding = 'utf-8') as f:
|
|
tokenizer = json.load(f)
|
|
if self.load_merges:
|
|
merges = tokenizer.get('model', {}).get('merges')
|
|
if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str):
|
|
self.merges = merges
|
|
tokenizer_config_file = path / 'tokenizer_config.json'
|
|
added_tokens = tokenizer.get('added_tokens')
|
|
if added_tokens is None or not tokenizer_config_file.is_file():
|
|
return True
|
|
with open(tokenizer_config_file, encoding = 'utf-8') as f:
|
|
tokenizer_config = json.load(f)
|
|
for typ in self.special_token_types:
|
|
entry = tokenizer_config.get(f'{typ}_token')
|
|
if isinstance(entry, str):
|
|
tc_content = entry
|
|
elif isinstance(entry, dict):
|
|
entry_content = entry.get('content')
|
|
if not isinstance(entry_content, str):
|
|
continue
|
|
tc_content = entry_content
|
|
else:
|
|
continue
|
|
for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content):
|
|
if isinstance(maybe_token_id, int) and maybe_token_id >= 0:
|
|
self.special_token_ids[typ] = maybe_token_id
|
|
break
|
|
return True
|
|
|
|
def _try_load_from_config_json(self, path: Path) -> bool:
|
|
config_file = path / 'config.json'
|
|
if not config_file.is_file():
|
|
return False
|
|
with open(config_file, encoding = 'utf-8') as f:
|
|
config = json.load(f)
|
|
for typ in self.special_token_types:
|
|
maybe_token_id = config.get(f'{typ}_token_id')
|
|
if isinstance(maybe_token_id, int) and maybe_token_id >= 0:
|
|
self.special_token_ids[typ] = maybe_token_id
|
|
return True
|
|
|
|
def add_to_gguf(self, gw: GGUFWriter) -> None:
|
|
if len(self.merges) > 0:
|
|
print(f'gguf: Adding {len(self.merges)} merge(s).')
|
|
gw.add_token_merges(self.merges)
|
|
for typ, tokid in self.special_token_ids.items():
|
|
handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
|
|
if handler is None:
|
|
print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping')
|
|
continue
|
|
print(f'gguf: Setting special token type {typ} to {tokid}')
|
|
handler(tokid)
|
|
|
|
def __repr__(self) -> str:
|
|
return f'<SpecialVocab with {len(self.merges)} merges and special tokens {self.special_token_ids or "unset"}>'
|
|
|
|
|
|
# Example usage:
|
|
if __name__ == "__main__":
|
|
# Example usage with a file
|
|
gguf_writer = GGUFWriter("example.gguf", "llama")
|
|
|
|
gguf_writer.add_architecture()
|
|
gguf_writer.add_block_count(12)
|
|
gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer
|
|
gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float
|
|
gguf_writer.add_custom_alignment(64)
|
|
|
|
tensor1 = np.ones((32,), dtype=np.float32) * 100.0
|
|
tensor2 = np.ones((64,), dtype=np.float32) * 101.0
|
|
tensor3 = np.ones((96,), dtype=np.float32) * 102.0
|
|
|
|
gguf_writer.add_tensor("tensor1", tensor1)
|
|
gguf_writer.add_tensor("tensor2", tensor2)
|
|
gguf_writer.add_tensor("tensor3", tensor3)
|
|
|
|
gguf_writer.write_header_to_file()
|
|
gguf_writer.write_kv_data_to_file()
|
|
gguf_writer.write_tensors_to_file()
|
|
|
|
gguf_writer.close()
|